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For peer review only

Association of individual and neighborhood socioeconomic status on physical activity and sedentary behavior in 7th

graders in Berlin, Germany

Journal: BMJ Open

Manuscript ID bmjopen-2017-017974

Article Type: Research

Date Submitted by the Author: 29-May-2017

Complete List of Authors: Krist, Lilian; Charité University Medical Center, Institute for Social Medicine, Epidemiology and Health Economics Bürger, Christin; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Ströbele-Benschop, Nanette; , University of Hohenheim, 3 Institute of Nutritional Medicine Roll, Stephanie; Charité Universitätsmedizin, Institute for Social Medicine, Epidemiology and Health Economics Lotz, Fabian; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Rieckmann, Nina; Charité Universitätsmedizin, Berlin School of Public Health Muller-Nordhorn, Jacqueline; Universitätsmedizin Berlin, Berlin School of Public Health Willich, Stefan; Charité University Medical Center Müller-Riemenschneider, Falk; National University of Singapore, Saw Swee HOck School of Public Health

<b>Primary Subject Heading</b>:

Public health

Secondary Subject Heading: Epidemiology, Sociology, Sports and exercise medicine

Keywords: EPIDEMIOLOGY, Community child health < PAEDIATRICS, PREVENTIVE MEDICINE, PUBLIC HEALTH, SPORTS MEDICINE, SOCIAL MEDICINE

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

BMJ Open on June 16, 2020 by guest. P

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ecember 2017. D

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1

Association of individual and neighborhood socioeconomic status on physi-

cal activity and sedentary behavior in 7th graders in Berlin, Germany

Lilian Krist1, Christin Bürger

1, Nanette Ströbele-Benschop

2, Stephanie Roll

1, Fabian Lotz

1,

Nina Rieckmann2, Jacqueline Müller-Nordhorn

2, Stefan N. Willich

1, Falk Müller-

Riemenschneider1,3

1. Institute for Social Medicine, Epidemiology and Health Economics, Charité - Universi-

tätsmedizin Berlin, Germany

2. Institute of Nutritional Medicine, University of Hohenheim, Germany

3. Institute of Public Health, Charité-Universitätsmedizin Berlin, Germany

4. Saw Swee Hock School of Public Health, National University of Singapore

Corresponding author:

Dr. Lilian Krist

Institute for Social Medicine, Epidemiology and Health Economics

Charité - Universitätsmedizin Berlin

Luisenstr. 57

10117 Berlin, Germany

E-Mail: [email protected]

Tel: +49 30 450 529 109

Word count: 2930

Keywords: neighborhood socioeconomic status, physical activity, sedentary behavior, adolescents

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ABSTRACT

Objectives Few studies have explored the impact of neighborhood socioeconomic status (SES) on

health behavior in youths. Our aim was to investigate the association of individual and neighborhood

socioeconomic status on physical activity (PA) and sedentary behavior (SB) in 12-13 years old stu-

dents in Berlin, Germany.

Design Cross-sectional study.

Setting Secondary schools (high schools and integrated secondary schools) in Berlin, Germany.

Participants A total of 2586 students aged 12-13 years (7th grade).

Main outcome measures Sociodemographics, anthropometric data and health behavior were assessed

by self-report during classes. Primary outcomes were daily leisure time PA and daily sedentary time.

Students’ characteristics were described with means or percentages. Comparisons were performed

with Generalized Linear Mixed Model yielding odds ratios with 95% confidence intervals.

Results Mean (±SD) age was 12.5±0.5 years, 50.5% were girls, 34.1% had a migrant background.

12.8% of the students were at least 60 minutes per day active, 74.1% spent more than two hours per

day sitting. Multivariable analysis showed that male sex, lower Body Mass Index (BMI), and attend-

ing an integrated secondary school were associated with higher levels of PA, whereas female sex, low-

er BMI, attending a high school and having a higher individual and neighborhood SES were associated

with less time spent in SB.

Conclusions Most of the students did not meet recommendations regarding PA and SB. Sex, BMI and

school type influenced both, PA and SB. SB was additionally influenced by individual and neighbor-

hood SES. Prevention strategies regarding PA promotion should focus rather on sex or school types

than on neighborhood SES.

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Strengths and limitations of the study

• This study provides important new insights into the association of neighborhood socioeconomic

status with physical activity and sedentary behavior in school students, independent of the indi-

vidual socioeconomic status.

• The study sample was very large with students recruited from all 12 districts of Berlin. It com-

prised a variety of neighborhoods with different levels of socioeconomic status. Also, the amount

of students with migration background was representative for the population of Berlin.

• Anthropometric data and physical activity were not assessed objectively but via self-report.

• Sedentary behavior was only assessed as screen time and did not include other behaviors like do-

ing homework, talking on the phone and sitting at school.

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INTRODUCTION

Physical activity has an important impact on health and wellbeing1. Low levels of physical activity are

associated with higher health risks already among children and adolescents2 and an increasing number

of studies have identified sedentary behavior as an independent risk factor for diseases such as diabe-

tes and obesity in children and adolescents3.

The guidelines of the World Health Organisation (WHO) recommend that children and adolescents

should spend at least 60 minutes every day in moderate to vigorous activity4. The American Academy

of Paediatrics (AAP) recommends that school-aged children and youth accumulate no more than 2

hours of recreational screen time each day5.

In the last decades however, sedentary behaviour among children and adolescents (watching TV or

playing computer games) is increasing while the rates of children being active (playing outside, doing

sports) appears to be decreasing over time6,7.

In most developed countries, including Germany, a decline in physical activity among children and

adolescents can be found with higher age8. The percentage of boys and girls in the German Health

Interview and Examination Survey for children and adolescents (KiGGS) fulfilling the WHO recom-

mendations for physical activity was 51.5% for the age group 3-6 years (boys 52.2%, girls 50.7%) and

11.5% in the age group 14-17 (boys 15.0%, girls 8.0%). Over half of the girls and nearly three quarters

of the boys spent more than two hours per day sitting while using computer, TV or video games7.

There is evidence on the association of age and sex on physical activity9, whereas studies exploring the

influence of socioeconomic status and built and social environment of children show heterogeneous

results10–12.

A low SES often is associated with a higher BMI and more sedentary time, but not always with low

physical activity13–15

. Studies investigating the social environment of children found evidence of an

association with physical activity and diet16–18. However, neighborhood socioeconomic status as one

aspect of the social environment has not been investigated sufficiently regarding physical activity.

Mechanisms through which a lower neighborhood socioeconomic status may influence physical ac-

tivity and sedentary behavior could be reduced municipal services such as recreational facilities and

playgrounds, financial stress or less possibilities to own a gym membership18

. Studies investigating the

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influence of neighborhood SES on health independently from the individual SES showed an associa-

tion of disadvantaged neighborhoods with worse health status19 or a higher risk for cardiovascular

diseases20.

The results of existing studies regarding physical activity and sedentary behavior are heterogeneous

reporting no association at all, only for special groups or only under certain circumstances21–23.

Our aim was therefore to investigate the influence of neighborhood socioeconomic status on physical

activity and sedentary behavior in a population based sample of 12 to 13 year old secondary school

students in Berlin, Germany.

METHODS

Study design and setting

The present cross-sectional analysis is part of the BEST-prevention study, a three armed cluster ran-

domized controlled trial that was conducted from 2010 to 2014 with the aim to evaluate a parent in-

volving smoking prevention program for 7th grade students in Berlin24. Here, we report cross-sectional

data regarding physical activity and sedentary behavior among the students at baseline.

Participants and Recruitment

Details of the recruitment are described elsewhere25

. Briefly, prior to recruitment, permission of the

Berlin senate of education, youth and research (Senatsverwaltung für Bildung, Jugend und Wissen-

schaft) was obtained, and school principals and contact teachers from all 12 districts of Berlin were

informed about the project. Students were eligible for the study if they: i) were in the 7th grade, ii)

attended one of the participating schools, and iii) showed intellectual and physical ability to make an

informed decision about study participation. Separate signed written informed consent was required

from participating students as well as from at least one parent/caregiver. The study was approved by

the ethical review committee of the Charité-Universitätsmedizin Berlin, Germany.

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Measurements

The study questionnaire is based on existing and validated questionnaires investigating adolescent

health behavior (e.g. Health Behavior in School Aged Children, HBSC26; German Children and

Youths Survey, KIGGS27). It includes questions related to socio-demographics, smoking and other

health behaviors, such as alcohol consumption, nutrition, physical activity and sedentary behaviors, as

well as height and weight. Our study group has the status of an associated project of the HBSC.

During a first visit, the BEST study was presented and consent forms were distributed to the students

by trained research personnel, during thesecond visit, baseline data were assessed with the question-

naire in an entire class.

Outcome measures

Physical activity

Physical activity (PA) was assessed using three adapted items of the HBSC questionnaire. The first

question was assessed by asking: ‘On how many days in the past week were you physically active for

at least 60 minutes?’ According to the WHO guidelines, for our primary outcome we defined a student

as meeting current guidelines if he or she was active at least 60 minutes on each of the last seven days

(yes/no)4. The other questions asked for the number of days and hours of physical activity per week.

Sedentary behavior

Sedentary behavior (SB) was assessed with two questions asking for the screen time of the students

per day (week day and weekend day were asked seperately). According to the AAP5 recommendations

we defined more than 2 hours sceen time per day as high sedentary behavior.

Covariates

Individual level

Sex, age and anthropometric data (height and weight) of the students were assessed via self-report.

The BMI was calculated using the self-reported data. Subgroups were chosen using the quasi-centile

curves (constructed to pass through a defined BMI at a given age) suggested by Cole et. al28,29

. We

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used the age of 18 years as reference since the BMI definition is valid from that age. According to that,

underweight was defined as a BMI at the age of 18 of under 18.5 kg/m², normal weight as a BMI be-

tween 18.5 and 24.9 kg/m², overweight as a BMI between 25 and 29.9 kg/m², and obesity as a BMI of

over 30 kg/m².

Migration background

A student was defined as having a migration background if he or she was not born in Germany or if at

least one parent was not born in Germany but moved to Germany after 194930.

Individual socioeconomic status

To assess the individual socioeconomic status (SES) of the student, we used the family affluence scale

(FAS), a validated instrument to assess the material affluence of the family asking for the number of

cars and computers in the family, for holidays during the past 12 months, and whether the child has its

own room31. The FAS consists of values from zero to seven, with higher values indicating higher af-

fluence, and can be categorized into three categories (low, moderate, and high affluence). The FAS

was completely assessed only at the 24 months follow-up, we therefore used the 24 months follow-up

FAS to describe family SES at baseline.

Neighborhood socioeconomic status

For the SES of the students’ neighborhood, we used the social index defined and implemented by the

‘Atlas of Social Structure’ (Sozialstukturatlas), an instrument used in Berlin to describe the social situ-

ation of Berlin by classifying postal code regions accordingly32,33. This social index reflects the distri-

bution of social and health burden in Berlin. Social and health indicators are e.g. unemployment, wel-

fare reception rate, average per capita income and also premature mortality and avoidable deaths. The

index ranges from 1 reflecting the best to 7 reflecting the worst social situation of a district.

School types

In Berlin, two types of schools exist: high schools with the possibility to achieve a high-school diplo-

ma after 12 years, as well as integrated secondary schools (an integration of different school types)

with the possibility to achieve a high-school diploma after 13 years. More often than high schools,

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integrated secondary schools are left by the students after the 10th grade with a secondary school leav-

ing certificate. The academic requirements are higher in high schools than in integrated secondary

schools34.

Schools’ neighborhood socioeconomic status

Analogous to the individual neighborhood social index, we assessed the social index of the schools’

neighborhood.

Statistical analysis

All statistical analyses were performed for the 12 and 13 years old students only due to the small num-

ber of students in the other age groups (2.6, 5.1, 0.3 and 0.1% in the age groups 11, 14, 15 and 16

years, respectively). We used all data available for the respective analysis; missing data were not im-

puted.

Variables were analysed by descriptive statistical methods (e.g. mean and standard deviation (SD),

frequencies and percentages).

Because of the nested structure of the data with both fixed and random effects, a generalized linear

mixed model (GLMM) was used for the analysis when comparing groups. In general, the random fac-

tors ‘school’ and ‘class within school’ (as nested factor) were included into the models. For binary

outcomes a logit link function was used, for continuous outcomes an identity link function was used

(distribution family ’normal‘). Results of logistic models are presented as odds ratios (OR) and 95%-

confidence intervals (CI), results of linear models as Least-Square Means and 95%- CI.

The same framework was used to determine the association of factors within a set of many factors in

multivariable analyses. Here, we included sex, socioeconomic status (FAS-score), and migration

background as categorical variables and BMI as continuous variable in our first model to investigate

individual influencing factors. In addition we included the respective outcome as covariate (sedentary

behavior as covariate for physical activity and vice versa). Afterwards we added in three steps the

environmental factors ‘student’s neighborhood SES’, ‘school type’ and ‘schools’ neighborhood SES’.

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Nominal p-values are reported and are considered exploratory. Analyses were performed using the

software package SAS release 9.3 and 9.4 (SAS Institute Inc., Cary, NC, USA).

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RESULTS

Baseline characteristics of the study population

We included 2586 students aged 12 and 13 years in our analysis. Figure 1 shows the recruitment pro-

cess of the schools, classes and students.

Sociodemographic baseline characteristics of all participating students are presented in Table 1a, the

characteristics of the schoos are presented in Table 1b. The mean (±SD) age of participants was 12.5 ±

0.5 years and the distribution between girls and boys was similar (50.5% vs. 49.5%). Of the entire

sample, 34.1% were defined as having a migrant background.

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Table 1a: Baseline Characteristics of Students

*Body Mass Index (kg/m2)

** BMI percentiles according to Cole et al. 35,36

*** Socioeconomic Status a High schools (5th or 7th grade to 12th grade, graduation with high school diploma after 12th grade) b Integrated secondary schools (integration of different school types, 7th grade to 13th grade, graduation with secondary school

leaving certificate after 10th grade or high school diploma after 13th grade)

Individual level Boys Girls Total p-value

Mean ± SD or n (%)

Number of students, n (%) 1279 (49.5) 1307 (50.5) 2586

Age (years, mean ± SD) (n=2586) 12.5 ± 0.5 12.4 ± 0.5 12.4 ± 0.5 <0.001

12 years, n (%) 651 (50.9) 775 (59.3) 1426 (55.1) <0.001

13 years, n (%) 628 (49.1) 532 (40.7) 1160 (44.9)

Height (cm, mean ± SD) (n=2440) 161.1±9.5 160.1±7.3 160.6 ± 8.4 0.003

Weight (kg, mean ± SD) (n=2360) 49.5 ± 10.9 47.0 ± 8.9 48.3 ± 10.0 <0.001

BMI* (kg/m2, mean ± SD) (n=2296) 18.9 ± 3.1 18.3 ± 2.7 18.6 ± 2.9 <0.001

BMI range 11.8 – 30.9 11.7 – 33.8 11.7 – 33.8

Underweight (BMI <10th percen-

tile)**

126 (11.1) 218 (18.8) 344 (15.0) <0.001

Normal weight (BMI 10th - <90th

percentile)**

822 (72.2) 843 (72.6) 1665 (72.4)

Overweight (BMI 90th - <97

th per-

centile)**

166 (14.6) 90 (7.8) 256 (11.1)

Obesity (BMI ≥97th percentile)** 24 (2.1) 10 (0.9) 34 (1.5)

Migrant background (n=2423) 396 (33.1) 429 (35.0) 825 (34.0) 0.307

Socioeconomic status (family affluence

scale) (n=2139)

6-7 (high SES***) 569 (53.7) 500 (46.3) 1069 (50.0) 0.003

4-5 (moderate SES***) 371 (35.0) 441 (40.9) 812 (38.0)

0-3 (low SES***) 120 (11.3) 138 (12.8) 258 (12.1)

Student’s neighborhood SES (n=2240) 1114 1126 2240

Mean±SD 4.0 ± 1.9 4.1 ± 1.9 4.0 ± 1.9 0.526

1 127 (11.4) 123 (10.9) 250 (11.2)

2 182 (16.3) 194 (17.2) 376 (16.8)

3 143 (12.8) 119 (10.6) 262 (11.7)

4 215 (19.3) 229 (20.3) 444 (19.8)

5 162 (14.5 ) 160 (14.2) 322 (14.4)

6 134 (12.0) 134 (11.9) 268 (12.0)

7 151 (13.6) 167 (14.8) 318 (14.2)

School type (n=2586)

High Schoola students (15 schools) 507 (39.6) 624 (47.7) 1131 (43.7) <0.001

Integrated Secondary Schoolb stu-

dents (32 schools)

772 (60.4) 683 (52.3) 1455 (56.3)

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Table 1b: Baseline characteristics of schools

* Socioeconomic Status aHigh schools (5th or 7th grade to 12th grade, graduation with high school diploma after 12th grade) bIntegrated secondary schools (integration of different school types, 7th grade to 13th grade, graduation with secondary school


School level

High Schoolsa Integrated Secondary

Schoolsb

Total

Schools’ neighborhood

SES*, Mean±SD 3.5±1.4 3.2±1.6 4.0±1.8 <0.001

n (%)

1 1 (6.7) 3 (9.4) 4 (8.5)

2 4 (26.7) 3 (9.4) 7 (14.9)

3 2 (13.3) 4 (12.5) 6 (12.8)

4 4 (26.7) 7 (21.9) 11 (23.4)

5 2 (13.3) 4 (12.5) 6 (12.8)

6 2 (13.3) 5 (15.6) 6 (12.8)

7 0 (0.0) 6 (18.8) 6 (12.8)

Student’s neighborhood

SES*, Mean±SD (n=2240) 3.1±1.6 4.6±1.9 <0.001

SES* (family affluence

scale) (n=2139)

6-7 (high SES*) 65.8% 36.5%

<0.001 4-5 (moderate SES*) 29.9% 44.8%

0-3 (low SES*) 4.3% 18.7%

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Physical activity and sedentary behavior

Of the total sample, 12.8% fulfilled the WHO criteria being active for at least 60 minutes per day. The

proportion of boys fulfilling the criteria was higher than in girls (15.9% of the boys vs. 9.8% of the

girls, OR 1.7 [1.4;2.2]; p<0.001) and boys were more active outside school than girls (0.9±0.8 versus

0.6±0.6 hours per day, mean difference 0.3 hours [0.2;0.3], p<0.001), Table 2. More than 80% of the

boys and almost two thirds of the girls reported sitting for more than 2 hours per day (outside of

school), OR 2.2 [1.8;2.6]; p<0.001. Sedentary behavior on weekend days was higher than on week

days among all students (5.7±3.6 versus 3.5±2.7).

Table 2. Comparison of boys and girls aged 12-13 years

Boys

Girls

p-value

(boys

vs. girls)

Total

Proportion Proportion Odds ratio [95%

CI]

Proportion

WHO recommendations fulfilled: 60min/day

every day per week (%)

15.9 9.8 1.7 (1.4;2.2) <0.001 12.8

Sitting 2 hours or more per day (%) 81.5 66.9 2.2 (1.8;2.6) <0.001 74.1

(hours ± SD per week) Mean±SD Mean±SD Mean difference

[95% CI]

p-value Mean±SD

Leisure time physical activity per day

(hours± SD)

0.9±0.8 0.6±0.6 0.3 (0.2;0.3) <0.001 0.8±0.7

Sedentary behavior TV (hours± SD)

school day 2.2 ± 1.8 1.9 ± 1.6 0.3 (0.2;0.4) <0.001 2.0 ± 1.7

weekend day 3.4 ± 2.0 2.9 ± 2.0 0.4 (0.3;0.6) <0.001 3.1 ± 2.0

Sedentary behavior Computer (hours± SD)

school day 1.8 ± 1.8 1.3 ± 1.6 0.5 (0.4;0.7) <0.001 1.5 ± 1.7

weekend day 3.1 ± 2.2 2.0 ± 2.0 1.1 (1.0;1.3) <0.001 2.6 ± 2.2

Overall sedentary behavior (hours± SD)

school day 3.9 ± 2.7 3.1 ± 2.5 0.8 (0.6;0.97) <0.001 3.5 ± 2.7

weekend day 6.5 ± 3.6 4.9 ± 3.4 1.6 (1.3;1.8) <0.001 5.7 ± 3.6

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Multivariable analysis

In model 1 of the multivariable analysis we included the individual variables sex, BMI, socioeconomic

status and migration background. Sex and BMI were statistically significantly associated with both

outcomes, physical activity and sedentary behavior (Figure 2 and 3; suppl. table 1 and 2). Boys had

higher odds to be active but at the same time they were also more likely to sit compared to girls. High-

er BMI was associated with less activity and more sedentary time. A higher socioeconomic status and

having no migration background were associated with less sedentary time but were not associated with

physical activity. We saw no association of sedentary behavior on physical activity (and vice versa).

There was no interaction effect between gender and sedentary behavior regarding physical activity,

nor between gender and physical activity regarding sedentary behavior (data not shown).

Subsequently, the students’ neighborhood SES (model 2a), school type (model 2b) and schools’

neighborhood SES (model 2c) were separately included in three different models. Students’ neighbor-

hood SES was not associated with physical activity, but students with a lower neighborhood SES were

more likely to sit more than two hours per day than students with a higher neighborhood SES. Stu-

dents of the integrated secondary schools were more active but reported also more sedentary time than

high school students. Schools’ neighborhood SES had no effect on physical activity nor on sedentary

behavior.

After including all covariates for model 3, physical activity was influenced by sex, BMI, and school

type, while sedentary behavior was additionally influenced by the individual and the neighborhood

socioeconomic status of the student. In summary: boys, students with a low BMI, and secondary

school students, were more likely to meet WHO recommendations regarding 60 minutes of physical

activity per day, and boys, students with a high BMI, secondary school students, and students with a

low individual or neighborhood socioeconomic status were more likely to exceed the 2 hours of seden-

tary time per day. To exclude eventual associations between school type and other outcomes we calcu-

lated one model without the covariate school type which did not change the results (data not shown).

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DISCUSSION

In our study sample, fewer than 20% of boys and 10% of the girls fulfilled the WHO recommenda-

tions for physical activity. These results are in line with the most recent results of the KiGGS Study

(KiGGS wave 1) and the German subsample of the HBSC study7,37.

More than 80% of the boys and 2/3 of the girls spent more than 2 hours per day sitting (in addition to

sitting time at school), which is exceeding international screen viewing recommendations5. In many

other countries, the numbers of children and adolescents engaging in more than 2 hours of screen time

per day are comparable with German youths8. The ENERGY project (participating countries were

Belgium, Greece, Hungary, the Netherlands and Switzerland) showed that girls aged 10-12 years spent

more time sitting than boys38.

Students of integrated secondary schools spent more time sitting, but were more active than high

school students. This is in contrast to a study from Germany, where high school students were more

likely to achieve a healthier lifestyle including regular physical activity than students from other

school types14

. The neighborhood SES as well as the SES of the students was correlated with the

school type which is a common fact in Germany39. However, school type remained an independent

influencing factor regarding physical activity, while neither individual nor neighborhood SES were

associated.

The SES of the students in our study sample was only significantly associated with sedentary behav-

ior, but not with physical activity. The HBSC data for Germany also did not show any influence of the

SES on physical activity, but a positive correlation between lower SES and higher sedentary time was

found37. However, the influence of family affluence is very heterogeneous across different countries.

In Eastern countries for instance, watching TV is associated with higher affluence, whereas in Western

and Northern countries, the opposite is the case8.

In contrast to the study of Lampert et al., students’ migration background had no influence on physical

activity in our study40

.

A new aspect of our study was the investigation of the neighborhood SES as influencing factor of

physical activity and sedentary behavior. Similar to the individual SES, an association was only found

for sedentary behavior, while physical activity was not affected.

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There is one study using the social index of Berlin, too, showing an association of a lower neighbor-

hood SES with a higher BMI in 5-6 years old children living in Berlin. The authors did not include,

however, health behaviors like physical activity or sedentary time in their analyses41.

Another study used unemployment rate and overcrowding as indicators of neighborhood SES. The

authors found a correlation between the unemployment rate and low physical activity in different ur-

ban districts in Germany and the Czech republic defining low physical activity as being active less

than once a week which is a very broad definition21

.

A study from Switzerland reported different settings while engaging in physical activity and sedentary

behavior among children of low and high neighborhood SES, whereas the overall time spent active or

sedentary did not differ essentially between the two groups22

.

Apparently, physical activity is hardly influenced by neighborhood SES, whereas sedentary behavior

is more likely to be affected. Up to now, the measurement of neighborhood SES is very heterogene-

ous. To further investigate this topic, a more detailed definition of neighborhood SES should be im-

plemented for European countries. Further research should focus on this to achieve a better compara-

bility of study results among different countries and to facilitate the investigation of its asscociations.

Strengths and limitations

Size and representativity of our sample including migration background, socioeconomic status and

gender distribution are important strenghts of our study42. However, the results are only valid for re-

gions with similar characteristics as Berlin: an urban region with high walkability and good infrastruc-

ture for transportation and cycling.

Some limitations have to be considered as well. Anthropometric data and physical activity were not

measured objectively. Self-report sometimes leads to distorted results through misreporting43

. Howev-

er, children and adolescents seem to be as reliable in providing accurate information as adults44. An-

other limitation is that we assessed sedentary behavior only as screen time and did not include other

behaviors like doing homework, talking on the phone or others.

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CONCLUSION

In our study sample, the majority of the students did not meet the WHO recommendations regarding

physical activity and sedentary time. Sex, BMI and school type influenced both, physical activity and

sedentary behavior. Sedentary behaviour was additionally influenced by individual and neighborhood

SES. Future prevention strategies regarding physical activity promotion should focus rather on differ-

ent approaches for boys and girls or schooltypes than on the neighborhood SES.

ACKNOWLEDGEMENTS

The authors thank the Berlin City Council (Senatsverwaltung für Bildung, Jugend und Wissenschaft,

Berlin) and the participating schools, teachers and students for supporting the project.

CONTRIBUTORS

LK drafted the manuscript with intellectual input from SR, NR, JMN, NSB and FMR, carried out the

data collection and parts of the data analysis. FL and SR were responsible for data assessment and

statistical analyses, CB, NR, JMN and NSB were responsible for the design of the recruitment meth-

ods and the assessment tools, SW and FMR participated in the conception of the study. All authors

read and approved the final manuscript.

FUNDING

This work was supported by ‘The German Cancer Aid (Deutsche Krebshilfe e.V.)’.

COMPETING INTERESTS

None declared.

ETHICS APPROVEL

The study was approved by the ethical review committee of the Charité-Universitätsmedizin Berlin,

Germany.

DATA SHARING STATEMENT

No additional data available.

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Pediatr. 2004;4(4):371.

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Figures

Figure 1. Flowchart of the recruitment process

Figure 2. Multivariable analysis of influencing factors on physical activity of 12 and 13 year old stu-

dents

Figure 3. Multivariable analysis of influencing factors on sedentary behavior of 12 and 13 years old

students

Supplementary files

Supplementary table 1. Multivariable analysis of influencing factors on physical activity of 12 and 13

year old students.

Supplementary table 2. Multivariable analysis of influencing factors on sedentary behavior of 12 and

13 year old students.

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108x154mm (96 x 96 DPI)

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Figure 2. Multivariable analysis of influencing factors on physical activity of 12 and 13 year old students

369x425mm (96 x 96 DPI)

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Figure 3. Multivariable analysis of influencing factors on sedentary behavior of 12 and 13 years old students

419x497mm (96 x 96 DPI)

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Supplementary table 1: Multivariable analysis of influencing factors on physical activity of 12 and 13 year old students.

WHO criteria for physical activity (at least 60 minutes per day physically active) fulfilled:

Model 1* (n=1595) Model 2a

(n=1381) Model 2b

(n=1595)

Model 2c** (n=1527) Model 3*** (n=1364)

OR (95% CI) p-value OR (95% CI) OR (95% CI) OR (95% CI) p-value OR (95% CI) p-value

Sex <0.001 <0.001 (2a,b,c) <0.001

Boys 1 1 1 1 1

Girls 0.51 (0.38; 0.69) 0.48 (0.35; 0.67) 0.52 (0.38;0.72) 0.50(0.37;0.69) 0.48 (0.34; 0.68)

BMI 0.90 (0.85; 0.95) <.001 0.92 (0.87; 0.98) 0.90 (0.85;0.95) 0.90 (0.85;0.95) 0.006 (2a); <0.001 (2b,c) 0.91 (0.86; 0.97) 0.003

Individual socioeconomic

status (FAS)

0.940 - 0.763 (2a); 0.684 (2b);

0.959 (2c) 0.476

High 1 1 1 1 1

Middle 0.95 (0.69; 1.31) 0.89 (0.62; 1.26) 0.87 (0.62;1.22) 0.95 (0.68;1.33) 0.83 (0.58; 1.20)

Low 1.01 (0.60; 1.69) 0.87 (0.50; 1.54) 0.86 (0.51;1.46) 0.97 (0.57;1.66) 0.74 (0.41; 1.33)

Migration background 0.844 0.588 (2a); 0.981 (2b);

0.798 (2c) 0.761

yes 1 1 1 1 1

no 0.97 (0.69; 1.36) 0.91 (0.63; 1.30) 1.00 (0.71;1.4) 0.96 (0.68;1.35) 0.94 (0.65; 1.37)

Sedentary behaviour 0.734 - 0.425 (2a); 0.540 (2b);

0.494 (2c) 0.233

≥2 hours 1 1 1 1 1

<2 hours 1.06 (0.76; 1.49) 1.16 (0.81; 1.67) 1.12 (0.78;1.60 1.13 (0.80;1.60) 1.26 (0.86; 1.86)

Student’s neighborhood

SES

- - - - 0.079 0.253

High (rank 1-2) - - 1 - - 1

Middle (rank 3-4) - - 1.27 (0.85; 1.88) - - 1.19 (0.78; 1.82)

Low (rank 5-7) - - 1.65 (1.07; 2.56) - - 1.51 (0.93; 2.46)

School type - - - - <0.001 0.002

High School - - - 1 - - 1

Integrated Secondary

School

- - - 2.12 (1.39; 3.23) - - 2.15 (1.34; 3.47)


SES

- - - - 0.546 0.723

High (rank 1-2) - - - - 1 - 1

Middle (rank 3-4) - - - - 1.26 (0.75; 2.11) - 1.15 (0.69; 1.92)

Low (rank 5-7) - - - - 1.33 (0.76; 2.32) - 0.92 (0.52; 1.62)

*Model 1: Sex, BMI, individual socioeconomic status (FAS), migration background, sedentary behavior; **Model 2a: Model 1+ student’s neighborhood SES (3 categories);

Model 2b: Model 1+ school type; Model 2c: Model 1 + schools’ neighborhood SES (3 categories); ***Model 3: Model 1+2a,b,c

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Supplementary table 2: Multivariable analysis of influencing factors on sedentary behavior of 12 and 13 year old students

High sedentary behaviour (≥2 hours /day)

Model 1* (n=1595) Model 2a**

(n=1381) Model 2b**

(n=1595) Model 2c**

(n=1527) Model 3*** (n=1364)

OR (95% CI) p-value OR (95% CI) OR (95% CI) OR (95% CI) p-value OR (95% CI) p-value

Sex <0.001 <0.001 (2a,b,c) <0.001

Boys 1 1 1 1 1

Girls 0.46 (0.37; 0.59) 0.42 (0.32; 0.54) 0.46 (0.37;0.59) 0.44 (0.35;0.56) 0.42 (0.33; 0.54)

BMI 1.09 (1.05; 1.14) <0.001 1.09 (1.04; 1.14) 1.09 (1.04;1.14) 1.10 (1.05;1.15) <0.001 (2a,b,c) 1.09 (1.04; 1.15) <0.001

Individual

socioeconomic status

(FAS)

0.004

0.007 (2a); 0,018 (2b);

0.014 (2c) 0.036

High 1 1 1 1 1 1

Middle 1.28 (1.00; 1.64) 1.32 (1.01; 1.73) 1.23 (0.96;1.57) 1.25 (0.97;1.61) 1.25 (0.95; 1.64)

Low 2.03 (1.30; 3.15) 2.09 (1.27; 3.45) 1.86 (1.18;2.93) 1.90 (1.20;3.01) 1.88 (1.12; 3.14)

Migration background 0.035 0.267 (2a); 0.052 (2b);

0.032 (2c) 0.262

yes 1 1 1 1 1

no 0.75 (0.57; 0.98) 0.85 (0.63; 1.14) 0.77 (0.59;1.00) 0.74 (0.56;0.98) 0.85 (0.63; 1.13)

Physical activity (WHO

criteria fulfilled) 0.738

0.510 (2a); 0.558 (2b);

0.533 (2c) 0.297

Yes 1 1 1 1 1

No 1.06 (0.75; 1.50) 1.14 (0.78; 1.66) 1.11 (0.78;1.58) 1.12 (0.79;1.59) 1.23 (0.84; 1.80)

Student’s

neighborhood SES - - Model 2a

- - 0.019

0.109

High (rank 1-2) - - 1 - - 1

Middle (rank 3-4) - - 1.49 (1.08; 2.05) - - 1.37 (0.99; 1.91)

Low (rank 5-7) - - 1.55 (1.10; 2.17) - - 1.40 (0.98; 2.00)

School type - - - Model 2b - 0.002 0.016

High School - - - 1 - - 1


School - - - 1.80 (1.25; 2.61)

- - 1.57 (1.09; 2.27)


SES - - -

- Model 2c 0.233 0.535

High (rank 1-2) - - - - 1 - 1

Middle (rank 3-4) - - - - 1.49 (0.94; 2.36) - 1.26 (0.82; 1.92)

Low (rank 5-7) - - - - 1.29 (0.79; 2.11) - 1.07 (0.67; 1.70)

*Model 1: Sex, BMI, individual socioeconomic status (FAS), migration background, physical activity; **Model 2a: Model 1+ student’s neighborhood SES (3 categories);

Model 2b: Model 1+ school type; Model 2c: Model 1 + schools’ neighborhood SES (3 categories); ***Model 3: Model 1+2a,b,c

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STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Checklist

A checklist of items that should be included in reports of observational studies. You must report the page number in your manuscript

where you consider each of the items listed in this checklist. If you have not included this information, either revise your manuscript

accordingly before submitting or note N/A.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published

examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web

sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology

at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

Section and Item Item No.

Recommendation Reported on

Page No.

Title and Abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the

abstract

(b) Provide in the abstract an informative and balanced summary of what was

done and what was found

Introduction

Background/Rationale 2 Explain the scientific background and rationale for the investigation being

reported

Objectives 3 State specific objectives, including any prespecified hypotheses

Methods

Study Design 4 Present key elements of study design early in the paper

Setting 5 Describe the setting, locations, and relevant dates, including periods of

recruitment, exposure, follow-up, and data collection

Participants 6 (a) Cohort study—Give the eligibility criteria, and the sources and methods of

selection of participants. Describe methods of follow-up

Case-control study—Give the eligibility criteria, and the sources and methods of

case ascertainment and control selection. Give the rationale for the choice of

cases and controls

Cross-sectional study—Give the eligibility criteria, and the sources and methods of

selection of participants

(b) Cohort study—For matched studies, give matching criteria and number of

exposed and unexposed

Case-control study—For matched studies, give matching criteria and the number

of controls per case

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and

effect modifiers. Give diagnostic criteria, if applicable

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http://www.plosmedicine.org/

http://www.annals.org/

http://www.epidem.com/

http://www.strobe-statement.org/





Page No.

Data Sources/

Measurement

8* For each variable of interest, give sources of data and details of methods of

assessment (measurement). Describe comparability of assessment methods if

there is more than one group

Bias 9 Describe any efforts to address potential sources of bias

Study Size 10 Explain how the study size was arrived at

Quantitative Variables 11 Explain how quantitative variables were handled in the analyses. If applicable,

describe which groupings were chosen and why

Statistical Methods 12 (a) Describe all statistical methods, including those used to control for

confounding

(b) Describe any methods used to examine subgroups and interactions

(c) Explain how missing data were addressed

(d) Cohort study—If applicable, explain how loss to follow-up was addressed

Case-control study—If applicable, explain how matching of cases and controls was

addressed

Cross-sectional study—If applicable, describe analytical methods taking account of

sampling strategy

(e) Describe any sensitivity analyses

Results

Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially

eligible, examined for eligibility, confirmed eligible, included in the study,

completing follow-up, and analysed

(b) Give reasons for non-participation at each stage

(c) Consider use of a flow diagram

Descriptive Data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and

information on exposures and potential confounders

(b) Indicate number of participants with missing data for each variable of interest

(c) Cohort study—Summarise follow-up time (eg, average and total amount)

Outcome Data 15* Cohort study—Report numbers of outcome events or summary measures over

time

Case-control study—Report numbers in each exposure category, or summary

measures of exposure

Cross-sectional study—Report numbers of outcome events or summary measures

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Page No.

Main Results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates

and their precision (eg, 95% confidence interval). Make clear which confounders

were adjusted for and why they were included

(b) Report category boundaries when continuous variables were categorized

(c) If relevant, consider translating estimates of relative risk into absolute risk for a

meaningful time period

Other Analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and

sensitivity analyses

Discussion

Key Results 18 Summarise key results with reference to study objectives

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or

imprecision. Discuss both direction and magnitude of any potential bias

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations,

multiplicity of analyses, results from similar studies, and other relevant evidence

Generalisability 21 Discuss the generalisability (external validity) of the study results

Other Information

Funding 22 Give the source of funding and the role of the funders for the present study and, if

applicable, for the original study on which the present article is based

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in

cohort and cross-sectional studies.

Once you have completed this checklist, please save a copy and upload it as part of your submission. DO NOT include this

checklist as part of the main manuscript document. It must be uploaded as a separate file.

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Association of individual and neighbourhood socioeconomic status with physical activity and screen time in 7th graders

in Berlin, Germany

Journal: BMJ Open

Manuscript ID bmjopen-2017-017974.R1


Date Submitted by the Author: 14-Aug-2017

Complete List of Authors: Krist, Lilian; Charité University Medical Center, Institute for Social Medicine, Epidemiology and Health Economics Bürger, Christin; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Ströbele-Benschop, Nanette; , University of Hohenheim, 3 Institute of Nutritional Medicine Roll, Stephanie; Charité Universitätsmedizin, Institute for Social Medicine, Epidemiology and Health Economics Lotz, Fabian; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Rieckmann, Nina; Charité Universitätsmedizin, Berlin School of Public Health Muller-Nordhorn, Jacqueline; Universitätsmedizin Berlin, Berlin School of Public Health Willich, Stefan; Charité University Medical Center Müller-Riemenschneider, Falk; National University of Singapore, Saw Swee HOck School of Public Health


Public health






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ecember 2017. D

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Association of individual and neighbourhood socioeconomic status with

physical activity and screen time in 7th graders in Berlin, Germany



2, Stephanie Roll

1, Fabian Lotz

1,



1, Falk Müller-

Riemenschneider1,3







Dr. Lilian Krist

Institute for Social Medicine, Epidemiology and Health Economics

Charité - Universitätsmedizin Berlin

Luisenstr. 57

10117 Berlin, Germany

E-Mail: [email protected]

Tel: +49 30 450 529 109

Word count: 3417

Keywords: neighbourhood socioeconomic status, physical activity, screen time, adolescents

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ABSTRACT

Objectives Few studies have explored the impact of neighbourhood socioeconomic status (SES) on

health behaviour in youths. Our aim was to investigate the association of individual and neighbour-

hood socioeconomic status with physical activity (PA) and screen time (ST) in 12-13 years old stu-

dents in Berlin, Germany.




Main outcome measures Sociodemographics, anthropometric data and health behaviour were as-

sessed by self-report during classes. Primary outcome was the association of individual and neigh-

bourhood SES with daily PA and daily ST. Students’ characteristics were described with means or

percentages. Comparisons were performed with Generalized Linear Mixed Model yielding odds ratios

with 95% confidence intervals.

Results Mean (±SD) age was 12.5±0.5 years, 50.5% were girls, and 34.1% had a migrant back-

ground.Individual SES was only associated with screen time. The odds ratio of engaging in more than

two hours of ST per day was 1.28 [1.00;1.64] and 2.03 [1.30;3.15] for students with middle and low

SES, respectively, compared to students with high SES. Neighbourhood SES was associated with

both, PA and ST. The odds ratios of spending more than 60 minutes per day in PA and of engaging in

more than two hours of ST per day in screen time were 1.67 [1.14;2.44] and 1.68 [1.22;2.29], respec-

tively, each for students with low compared to high neighbourhood SES.

Conclusions Both, individual and neighbourhood SES as well as school type are important factors that

have to be considered, when developing prevention programs for school students. Future research

should include measurement of the built environment in Berlin to provide further insights into the

associations with PA.

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• This study provides important new insights into the association of individual and neighbourhood

socioeconomic status with physical activity and screen time in school students in Berlin, Germany.

• The study sample was very large with students recruited from all 12 districts of Berlin. It com-

prised a variety of neighbourhoods with different levels of socioeconomic status. Also, the amount

of students with migration background reflected the proportion of the student population of Berlin.

• Anthropometric data and physical activity were not assessed objectively but via self-report.

• Only screen time was assessed, while other kinds of sedentary behaviours were not taken into

account.

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INTRODUCTION

Physical activity as well as sedentary beaviour have an important impact on health and wellbeing(1).

Low levels of physical activity are associated with higher health risks already among children and

adolescents(2) and an increasing number of studies have identified sedentary behaviour as an inde-

pendent risk factor for diseases such as diabetes and obesity in children and adolescents(3).

In the last decades however, sedentary behaviour among children and adolescents is increasing while

the rates of children being active appears to be decreasing over time(4,5). Screen time (time spent

watching TV or playing games on the computer or playing video games) is one important aspect of

sedentary behaviour, even though it does not represent the overall sedentary time (6).

In most developed countries, including Germany, a decline in physical activity among children and

adolescents can be found with higher age(7). The percentage of boys and girls in the German Health

Interview and Examination Survey for children and adolescents (KiGGS) fulfilling the WHO recom-

mendations for physical activity was 51.5% for the age group 3-6 years (boys 52.2%, girls 50.7%) and

11.5% in the age group 14-17 (boys 15.0%, girls 8.0%). Over half of the girls and nearly three quarters

of the boys spent more than two hours per day sitting while using computer, TV or video games(5).

While there is evidence of anassociation of age and sex with physical activity(8), studies exploring the

influence of socioeconomic status and built and social environment of children show heterogeneous

results(9–11).

A low SES is often associated with a higher BMI and more sedentary time, but not always with low

physical activity(12–14). Studies investigating the social environment of children found evidence of

an association with physical activity and diet(15–17).

Another aspect of the social environment is the neighbourhood socioeconomic status. Studies investi-

gating the influence of neighbourhood SES on health showed an association of disadvantaged neigh-

bourhoods with worse health status(18) or a higher risk for cardiovascular diseases(19). Mechanisms

through which a lower neighbourhood socioeconomic status may influence physical activity and sed-

entary behaviour could be reduced municipal services such as recreational facilities and playgrounds,

financial stress or less possibilities to own a gym membership(17). Regarding physical activity or sed-

entary behaviour, study results are heterogenous ranging from no association to a clear association

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with neighbourhood SES (20–22). Other studies in turn found that the neighbourhood SES was a posi-

tive modifier for the association of environmental factors with physical activity and sedentary behav-

iour(23,24). Knowing more about the influence of SES would make it possible to develop more tar-

geted prevention strategies for vulnerable groups.

Our aim was therefore to investigate the influence of individual and neighbourhood socioeconomic

status with physical activity and screen time as one important form of sedentary behaviour in a popula-

tion based sample of 12 to 13 year old secondary school students in Berlin, Germany.

METHODS



domized controlled trial that was conducted from 2010 to 2014 (baseline assessment was conducted

from 2010 to 2011) with the aim to evaluate a parent involving smoking prevention program for 7th

grade students in Berlin(25). Here, we report cross-sectional data regarding physical activity and

screen time among the students at baseline including associations with individual and neighbourhood

socioeconomic status.


Details of the recruitment are described elsewhere(26). Briefly, prior to recruitment, permission of the








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Measurements


health behaviour (e.g. Health Behaviour in School Aged Children, HBSC(27); German Children and

Youths Survey, KIGGS(28)). It includes questions related to socio-demographics, smoking and other

health behaviours, such as alcohol consumption, nutrition, physical activity and screen time, as well as

height and weight. It took about 30-40 minutes to complete the questionnaire. Our study group has the

status of an associated project of the HBSC.

During a first visit to schools, the BEST study was presented to the students by trained research per-

sonnel and consent forms were distributed for students and parents/caregivers. During the second visit,

which took place a few weeks later, baseline data were assessed with the questionnaire in the class-

room among children, who had provided both consent forms..

Outcome measures

Physical activity

Physical activity (PA) was assessed using three adapted items of the HBSC questionnaire. The first

question was assessed by asking: ‘On how many days in the past week were you physically active for

at least 60 minutes?’ According to the WHO guidelines, for our primary outcome we defined a student

as meeting current guidelines if he or she was active at least 60 minutes on each of the last seven days

(yes/no)(29). The other questions asked for the number of days and hours of moderate intensity physi-

cal activity per week.

Screen time

Screen time (ST) was assessed with two questions (as well of the HBSC questionnaire) asking for the

time spent each day watching TV or playing with the Computer. TV time was assessed by asking

‘How many hours/day do you usually watch television in your free time?’ for weekdays and weekend

days separately. Computer time (minutes/day) was assessed by asking ‘How many hours/day do you

usually play games on a computer, or use a game console in your leisure time?’. Total screen time was

computed by adding up TV and computer time. Using a smartphone or tablet was not assessed. Ac-

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cording to the AAP(30) recommendations we defined more than 2 hours of screen time per day as

high screen time.

Covariates

Individual level


The BMI was calculated using the self-reported data. BMI categories are presented using cut-offs de-

fined by the specific percentiles which at age 18 years correspond to the adult cut-off points for un-

derweight (<18.5kg/m2), overweight (25kg/m

2) and obesity (30kg/m

2). According to that definition,

underweight is defined as a BMI <10th percentile, normal weight as a BMI between the 10

th and the

90th percentile, overweight as a BMI between the 90th and the 97th percentile and obesity as a BMI

≥97th percentile(31,32).

Migration background

A student was defined as having a migration background if he or she was not born in Germany or if at

least one parent was not born in Germany but moved to Germany after 1949(33).





own room(34). The FAS consists of values from zero to seven, with higher values indicating higher

affluence, and can be categorized into three categories (low (0-3), moderate (4-5), and high affluence

(6-7)). The FAS was completely assessed only at the 24 months follow-up, we therefore used the 24

months follow-up FAS to describe family SES at baseline.

Neighbourhood socioeconomic status

For the SES of the students’ neighbourhood, we used the social index defined and implemented by the

‘Atlas of Social Structure’ (Sozialstukturatlas). It is an instrument used in Berlin to describe the social

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situation of Berlin by classifying 447 sub-areas (with on average 7500 habitants) of the 12 districts of

Berlin accordingly(35,36). This social index reflects the distribution of social and health burden in

Berlin. Social and health indicators are e.g. unemployment, welfare reception rate, average per capita

income and also premature mortality and avoidable deaths. The index ranges from 1 reflecting the best

to 7 reflecting the worst social situation of a district.

School types

In Berlin, two types of schools exist: high schools with the possibility to achieve a high-school diplo-

ma after 12 years, as well as integrated secondary schools (an integration of different school types)

with the possibility to achieve a high-school diploma after 13 years. More often than high schools,

integrated secondary schools are left by the students after the 10th grade with a secondary school leav-

ing certificate. The academic requirements are higher in high schools than in integrated secondary

schools(37).

Schools’ neighbourhood socioeconomic status

.Since the neighbourhood of the school can be different to that of students, we assessed this infor-

mation (analogous to the individual neighbourhood SES) as well in order to take an additional influ-

encing factor of the students behavor into account.


All statistical analyses were performed for the 12 and 13 years old students due to the small number of

students younger than 12 and older than 13 years (8.1%). We used all data available for the respective

analysis; missing data were not imputed.

Characteristics of schools and students were analysed by descriptive statistical methods (e.g. mean and

standard deviation (SD), frequencies and percentages; p-values are derived from t-tests and chi-square-

tests).


mixed model (GLMM) was used for the analysis when comparing groups (models with random inter-

cept). In general, the random factors ‘school’ and ‘class within school’ (as nested factor) were includ-

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ed into the models. For binary outcomes a logit link function was used, for continuous outcomes an

identity link function was used (distribution family ’normal‘). Results of logistic models are presented

as odds ratios (OR) and 95%-confidence intervals (CI), results of linear models as Least-Square

Means and 95%- CI. As sensitivity analyses, to assess if associations are modified by gender, interac-

tion effects on gender were included into the models.

The same framework was used to determine the association of factors within a set of many factors in

multivariable analyses. Here, we included sex, socioeconomic status (FAS-score), and migration

background as categorical variables and BMI as continuous variable in our first model to investigate

individual influencing factors. In addition we included the respective outcome as covariate (screen

time as covariate for physical activity and vice versa). The same procedure was performed for neigh-

bourhood SES. Afterwards we added in two steps the environmental factors ‘school type’ and

‘schools’ neighbourhood SES’. All p-values are considered exploratory (with no adjustment for multi-

ple testing). Analyses were performed using the software package SAS release 9.3 and 9.4 (SAS Insti-

tute Inc., Cary, NC, USA).

RESULTS

Characteristics of the study population

Out of 214 contacted schools, 49 schools (23%; 4291 students) showed interest and were eligible for

study participation. Before baseline assessment, 1268 out of these 4291 students dropped out including

two entire schools. 2801 students participated at the baseline assessment. Out of those, we included

2586 students aged 12 and 13 years in our analysis. Figure 1 shows the recruitment process of the

schools, classes and students.

Sociodemographic baseline characteristics of all participating students are presented in Table 1a, the

characteristics of the schools are presented in Table 1b. The mean (±SD) age of participants was 12.5

± 0.5 years and the distribution between girls and boys was similar (50.5% vs. 49.5%). Of the entire

sample, 34.1% were defined as having a migrant background.



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girls, OR 1.7 [1.4;2.2]; p<0.001) and boys were more active outside school than girls (0.9±0.8 versus

0.6±0.6 hours per day, mean difference 0.3 hours [0.2;0.3], p<0.001). More than 80% of the boys and

almost two thirds of the girls reported more than 2 hours ST per day, OR 2.2 [1.8;2.6]; p<0.001.

Screen time on weekend days was higher than on week days among all students (5.7±3.6 versus

3.5±2.7) (Table 1c).

Table 1a: Characteristics of the study sample


Mean ± Standard Deviation (SD) or n (%)


Age (years, mean ± SD) (n=2586) 12.5 ± 0.5 12.4 ± 0.5 12.4 ± 0.5 <0.001

12 years, n (%) 651 (50.9) 775 (59.3) 1426 (55.1) <0.001

13 years, n (%) 628 (49.1) 532 (40.7) 1160 (44.9)

Height (cm, mean ± SD) (n=2440) 161.1±9.5 160.1±7.3 160.6 ± 8.4 0.003

Weight (kg, mean ± SD) (n=2360) 49.5 ± 10.9 47.0 ± 8.9 48.3 ± 10.0 <0.001

BMI* (kg/m2, mean ± SD) (n=2296) 18.9 ± 3.1 18.3 ± 2.7 18.6 ± 2.9 <0.001

BMI range 11.8 – 30.9 11.7 – 33.8 11.7 – 33.8

Underweight (BMI <10th percentile)** 126 (11.1) 218 (18.8) 344 (15.0) <0.001

Normal weight (BMI 10th - <90th per-

centile)**

822 (72.2) 843 (72.6) 1665 (72.4)

Overweight (BMI 90th - <97th percen-

tile)**

166 (14.6) 90 (7.8) 256 (11.1)


Migrant background (n=2423) 396 (33.1) 429 (35.0) 825 (34.0) 0.307

Individual SES*** (family affluence scale;

FAS) (n=2139)

high (FAS 6-7) 569 (53.7) 500 (46.3) 1069 (50.0) 0.003

moderate (FAS 4-5) 371 (35.0) 441 (40.9) 812 (38.0)

low (FAS 0-3) 120 (11.3) 138 (12.8) 258 (12.1)

Student’s neighbourhood SES (n=2240) 1114 1126 2240

Mean±SD 4.0 ± 1.9 4.1 ± 1.9 4.0 ± 1.9 0.526

1 (best) 127 (11.4) 123 (10.9) 250 (11.2)

2 182 (16.3) 194 (17.2) 376 (16.8)

3 143 (12.8) 119 (10.6) 262 (11.7)

4 215 (19.3) 229 (20.3) 444 (19.8)

5 162 (14.5 ) 160 (14.2) 322 (14.4)

6 134 (12.0) 134 (11.9) 268 (12.0)

7 (worst) 151 (13.6) 167 (14.8) 318 (14.2)


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(descriptive statistical methods) *Body Mass Index (kg/m2)

** BMI percentiles according to Cole et al. (38,39)

*** Socioeconomic Status a High schools (5th or 7th grade to 12th grade, graduation with high school diploma after 12th grade) b Integrated secondary schools (integration of different school types, 7th grade to 13th grade, graduation with secondary school


Table 1b: Characteristics of schools

(descriptive statistical methods) SES: Socioeconomic Status, SD: Standard deviation aHigh schools (5th or 7th grade to 12th grade, graduation with high school diploma after 12th grade) bIntegrated secondary schools (integration of different school types, 7th grade to 13th grade, graduation with secondary school leaving certificate after 10th grade or high school diploma after 13th grade)


Integrated Secondary Schoolb students

(32 schools)

772 (60.4) 683 (52.3) 1455 (56.3)

School level

High Schoolsa Integrated Secondary

Schoolsb

Total

Schools’ neighbourhood

SES, Mean±SD (n=47) 3.5±1.4 3.2±1.6 4.0±1.8 <0.001

n (%)

1 (best) 1 (6.7) 3 (9.4) 4 (8.5)

2 4 (26.7) 3 (9.4) 7 (14.9)

3 2 (13.3) 4 (12.5) 6 (12.8)

4 4 (26.7) 7 (21.9) 11 (23.4)

5 2 (13.3) 4 (12.5) 6 (12.8)

6 2 (13.3) 5 (15.6) 6 (12.8)

7 (worst) 0 (0.0) 6 (18.8) 6 (12.8)

Student’s neighbourhood

SES*, Mean±SD (n=2240) 3.1±1.6 4.6±1.9 <0.001

Individual SES* (family

affluence scale; FAS)

(n=2139)

n (%)

High (FAS 6-7) 744 (65.8) 531 (36.5)

<0.001 Moderate (FAS 4-5) 338 (29.9) 652 (44.8)

Low (FAS 0-3) 49 (4.3) 272 (18.7)

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Table 1c. Comparison of boys and girls aged 12-13 years

Boys

Girls

p-value

(boys

vs. girls)

Total

Proportion Proportion Odds ratio [95%

CI]

Proportion

WHO recommendations fulfilled: 60min/day

every day per week (%) (n=2517)

15.9 9.8 1.7 (1.4;2.2) <0.001 12.8

Screen time of 2 hours or more per day (%)

(n=2503)

81.5 66.9 2.2 (1.8;2.6) <0.001 74.1

Mean±SD Mean±SD Mean difference

[95% CI]

p-value Mean±SD

Time spent with physical activity per day

(hours, mean±SD) (n=2517)

0.9±0.8 0.6±0.6 0.3 (0.2;0.3) <0.001 0.8±0.7

behaviourScreen time (TV) (hours,

mean±SD)

school day (n=2555) 2.2 ± 1.8 1.9 ± 1.6 0.3 (0.2;0.4) <0.001 2.0 ± 1.7

weekend day (n=2550) 3.4 ± 2.0 2.9 ± 2.0 0.4 (0.3;0.6) <0.001 3.1 ± 2.0

behaviourScreen time (Computer) (hours,

mean±SD)

school day (n=2558) 1.8 ± 1.8 1.3 ± 1.6 0.5 (0.4;0.7) <0.001 1.5 ± 1.7

weekend day (n=2541) 3.1 ± 2.2 2.0 ± 2.0 1.1 (1.0;1.3) <0.001 2.6 ± 2.2

Overall behaviourscreen time (hours,

mean±SD) (n=)

school day (n=2536) 3.9 ± 2.7 3.1 ± 2.5 0.8 (0.6;0.97) <0.001 3.5 ± 2.7

weekend day (n=2518) 6.5 ± 3.6 4.9 ± 3.4 1.6 (1.3;1.8) <0.001 5.7 ± 3.6

(descriptive statistical methods)

SD: Standard deviation, CI: Confidence interval

Association of individual and neighbourhood SES with PA and ST

In model 1 of the multivariable analysis we included the individual SES with and without adjustment

for sex, BMI, migration background and the respective outcome.

In model 2, we performed an analogous analysis for the student’s neighbourhood SES. In model 3a we

included individual and neighbourhood SES and the adjustment variables mentioned above. We then

included stepwise “school type” (model 3b) and schools’ neighbourhood SES (model 3c).

Physical activity was not associated with the individual SES, however, students with a low neighbour-

hood SES had a higher Odds (1.67 [1.14;2.44]; p=0.028) of spending at least 60 minutes per day being

physically activie compared to students with a high neighbourhood SES.

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If both types of SES were included in the model (model 3), this association was no longer statistically

significant. Adding school type and schools’ neighbourhood SES did not change that. Sex, BMI and

school type were statistically significantly associated with physical activity (Figure 2; suppl. table 1).

Screen time, was associated with individual SES in all models. The lower the students’ SES the higher

the Odds to spent more than two hours of ST per day (1.25 [0.95; 1.64] and

1.88 [1.12; 3.14]; p=0.036 for middle and low individual SES, respectively, compared to high

SES).The association with the neighbourhood SES showed the same trend. Students with lower neigh-

bourhood SES were more likely to spend more than two hours of ST per day than students with a high

neighbourhood SES. Again, after including school type and schools’ neigbourhood SES (model 3b

and 3c) the association was not statistically significant any more (Figure 3; suppl table 2).

Boys had a higher odds to be active but at the same time they were also more likely to engage in high-

er ST compared to girls. Higher BMI was associated with less activity and more screen time. We saw

no association of screen time with physical activity (and vice versa). There was no interaction effect

between gender and screen time regarding physical activity, nor between gender and physical activity

regarding screen time (data not shown).

Students of the integrated secondary schools were more active but reported also more screen time than

high school students. Schools’ neighbourhood SES had no effect on physical activity nor on screen

time.

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DISCUSSION

In this study we investigated the association of individual and neighbourhood SES with physical activ-

ity and screen time among school students. The individual SES of the students in our study sample,

measured with the family affluence scale, was significantly associated with screen time. Students with

lower SES were more likely to spend more than 2 hours per day viewing screen devices. Low SES

was stronger associated with screen time than middle SES, compared to high SES. Physical activity,

however, was not associated with the individual SES. This is in line with other studies which showed

that the individual SES is not a strong predictor of high PA among youths(40–42). A possible explana-

tion for these results is that PA consists not only of organised sports or activities that require a club

membership. A large part of PA among youths are daily life activities and are based on activities in the

neighbourhood and in parks which is independent from the individual SES(43).Another aspect of our

study was the investigation of the neighbourhood SES as an influencing factor of physical activity and

screen time. In contrast to the individual SES, an association was found for physical and screen time.

After including school type, the association remained statistically significant only for screen time.

Probably there was some interaction between the student’s neighbourhood SES and the school type,

which is quite probable since there are more integrated secondary schools in neighbourhoods with

lower SES than high schools. Also, it is known, that the neighbourhood SES as well as the SES of the

students is often correlated with the school type (44).In one previous study also using the social index

for Berlin, an association of a lower neighbourhood SES with a higher BMI in 5-6 years old children

living in Berlin was observed. However, the authors did not include health behaviours like physical

activity or sedentary time in their analyses(45).

It is possible that other factors like the built environment play a more important role than individual or

neighbourhood SES as factors influencing physical activity. Sallis et al. have shown in a study among

adults, that the number of public transport stops, residential density, intersection density and the num-

ber of parks were independently and positively associated with the time spent in physical activity(46).

Other authors also found associations of the built environment and physical activity(24). In another

study associations appeared to differ between population groups (persons with low neighbourhood

SES had a bigger benefit of a good walkability than those with a high neighbourhood SES)(23). Future

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research should include measurement of the built environment in Berlin to provide new insights into

the associations with physical activity.

In another previous study unemployment rate and overcrowding were used as indicators of neighbour-

hood SES. The authors found a correlation between the unemployment rate and low physical activity

in different urban districts in Germany and the Czech republic defining low physical activity as being

active less than once a week which is a very broad definition(20).

A study from Switzerland reported different settings while engaging in physical activity and sedentary

behaviour among children of low and high neighbourhood SES, whereas the overall time spent active

or sedentary did not differ substantially between the two groups(21).

In the present study, the association of individual and neighbourhood SES with screen time is more

consistent than the association with physical activity. For future health promotion programs different

school types may need to be taken into account in addition to differences in SES. In this study, stu-

dents of integrated secondary schools spent more time viewing screen devices, but were also more

active than high school students. This is in contrast to another study from Germany, where high school

students were more likely to achieve a healthier lifestyle, including regular physical activity than stu-

dents from other school types(13). According to our study results, integrated secondary school stu-

dents could benefit by interventions which promotes alternatives to screen time, whereas high school

students seem to need physical activity promotion.

In contrast to the study by Lampert et al., there was no association of students’ migration background

and physical activity in our study(47).


Strengths of our study include the size of our sample, as well as the proportion of students with migra-

tion background, socioeconomic status and gender distribution, which appear to be very similar to the

student population of Berlin(48). However, the results are only valid for regions with similar charac-

teristics as Berlin: an urban region with high walkability and good infrastructure for transportation and

cycling.

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Some limitations have to be considered as well. First, FAS was only assessed at the 24 month follow

up. However, we assessed one item of the FAS (holiday) both at baseline and at the 12 month follow-

up. The answers were quite similar over the two years. We thus think the period of two years impli-

cates only minimal changes in the FAS level. Anthropometric data and physical activity were not

measured objectively. Self-report may lead to distorted results through misreporting(49). However,

children and adolescents seem to be reliable in providing accurate and valid information as long as the

questionnaires are developed for the respective age group, which was the case(50). Another limitation

is that as screen time measures, only the use of TV, Computer and video games was assessed by the

applied HBSC questionnaire. Other kinds of divices (smartphones, tablets) and other kinds of seden-

tary behaviours like sitting during homework, talking on the phone and sitting at school were not taken

into account, which may have led to an underestimation of the screen time.

CONCLUSION

Lower individual and neighbourhood SES were independently associated with higher screen time in

students. Physical activity was not associated with the individual SES, but with neighborhood SES.

Both, individual and neighbourhood SES as well as school type are important factors that have to be

considered, when developing prevention programs for school students. Future research should include

measurement of the built environment in Berlin to provide new insights into associations with physical

activity.

ACKNOWLEDGEMENTS



CONTRIBUTORS

LK and FMR drafted the manuscript with intellectual input from SR, NR, JMN, and NSB. FMR su-

pervised the study and LK carried out the data collection and parts of the data analysis. FL and SR

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were responsible for data assessment and statistical analyses, FMR, CB, NR, JMN and NSB were re-

sponsible for the design of the recruitment methods and the assessment tools, FMR, NR, JMN, and

SW conceptualized the study. All authors read and approved the final manuscript.

FUNDING


COMPETING INTERESTS

None declared.

ETHICS APPROVEL

The study was approved by the ethical review committee of the Charité-Universitätsmedizin Berlin,

Germany.



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Figures


Figure 2. Multivariable analysis of physical activity associated factors among 12 and 13 years old

students

Figure 3. Multivariable analysis of screen time associated factors among 12 and 13 years old students

Supplementary files

Supplementary table 1. Multivariable analysis of physical activity associated factors among 12 and 13

years old students

Supplementary table 2. Multivariable analysis of screen time associated factors among 12 and 13 years

old students.

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Flowchart of the recruitment process

254x338mm (300 x 300 DPI)

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Multivariable analysis of screen time associated factors among 12 and 13 years old students

380x331mm (300 x 300 DPI)

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Supplementary table 1: Multivariable analysis of physical activity associated factors among 12 and 13 years old students


Model 1* Model 2** Model 3***

Model 1a (n=1926) Model 1b (n=1595) Model 2a (n=2027) Model 2b (n=1652) Model 3a (n=1381) Model 3b (n=1381) Model 3c (n=1364)

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

Individual socioeconomic

status (FAS) 0.640 0.940 - - - - 0.763 0.520 0.476

High 1 1 - - - - 1 1 1

Middle 0.90

(0.67; 1.19)

0.95

(0.69; 1.31) - - - -

0.89

(0.62; 1.26)

0.83

(0.58;1.19)

0.83

(0.58; 1.20)

Low 0.84

(0.53; 1.31)

1.01

(0.60; 1.69) - - - -

0.87

(0.50; 1.54)

0.78

(0.44;1.38)

0.74

(0.41; 1.33)

Student’s neighbourhood

SES - - - - 0.064 - 0.028 0.079 0.301 0.253

High (rank 1-2) - - - - 1 1 1 1 1

Middle (rank 3-4) - - - - 1.19

(0.83;1.71)

1.24

(0.84;1.84)

1.27

(0.85; 1.88)

1.20

(0.77;1.88)

1.19

(0.78; 1.82)

Low (rank 5-7) - - - - 1.49

(1.06;2.11)

1.67

(1.14;2.44)

1.65

(1.07; 2.56)

1.43

(0.91;2.24)

1.51

(0.93; 2.46)

Sex - - <0.001 - - <0.001 <0.001 <0.001 <0.001

Boys - - 1 - - 1 1 1 1

Girls - - 0.51

(0.38; 0.69) - -

0.48

(0.36;0.65)

0.48

(0.35; 0.67)

0.49

(0.35;0.69)

0.48

(0.34; 0.68)

BMI - - 0.90

(0.85; 0.95) <0.001 - -

0.92

(0.87;0.97) 0.002

0.92

(0.87; 0.98) 0.006

0.92

(0.86;0.97) 0.003

0.91

(0.86; 0.97) 0.003

Migration background - - 0.844 - - 0.957 0.588 0.621 0.761

yes - - 1 - - 1 1 1 1

no - - 0.97

(0.69; 1.36) - -

0.99

(0.72;1.36)

0.91

(0.63; 1.30)

0.91

(0.63;1.31)

0.94

(0.65; 1.37)

Screen time - - 0.734 - - 0.165 - 0.425 0.352 0.233

≥2 hours - - 1 - - 1 1 1 1

<2 hours - - 1.06

(0.76; 1.49) - -

1.26

(0.91;1.74)

1.16

(0.81; 1.67)

1.20

(0.82;1.75)

1.26

(0.86; 1.86)

School type - - - - - - - - - 0.006 0.002

High School - - - - - - - - - 1 1


School - - - - - - - - -

1.93

(1.21;3.07)

2.15

(1.34; 3.47)

Schools’ neighbourhood

SES - - - - - - - - - - 0.723

High (rank 1-2) - - - - - - - - - - 1

Middle (rank 3-4) - - - - - - - - - - 1.15

(0.69; 1.92)

Low (rank 5-7) - - - - - - - - - - 0.92

(0.52; 1.62)

*Model 1 (Model 1a: individual socioeconomic status (FAS), Model 1b: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, screen time)

**Model 2 (Model 2a: student’s neighbourhood SES (3 categories); Model 2b: student’s neighbourhood SES (3 categories) adjusted for Sex, BMI, migration background, screen time)

***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+School type, Model 3b: Model 1+2+School type+School neighbourhood SES

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Supplementary table 2: Multivariable analysis of screen time associated factors among 12 and 13 years old students


Model 1a (n=1925) Model 1b (n=1595) Model 2a (n=2014) Model 2b (n=1652) Model 3a (n=1381) Model 3b (n=1381) Model 3c (n=1364)

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

OR

(95% CI) p-value

Individual SES (FAS) 0.005 0.004 - - - - 0.007 0.019 0.036

High 1 1 - - - - 1 1 1

Middle 1.32

(1.06;1.65)

1.28

(1.00; 1.64) - - - -

1.32

(1.01; 1.73)

1.28

(0.97;1.67)

1.25

(0.95; 1.64)

Low 1.67

(1.17;2.39)

2.03

(1.30; 3.15) - - - -

2.09

(1.27; 3.45)

1.96

(1.18;3.26)

1.88

(1.12; 3.14)

Student’s

neighbourhood SES - - - - 0.005 0.002 0.019 0.057 0.109

High (rank 1-2) - - - - 1 1 1 1 1

Middle (rank 3-4) - - - - 1.14

(0.87;1.50)

1.07

(0.80;1.44)

1.49

(1.08; 2.05)

1.02

(0.74;1.39)

1.37

(0.99; 1.91)

Low (rank 5-7) - - - - 1.62

(1.23;2.15)

1.68

(1.22;2.29)

1.55

(1.10; 2.17)

1.45

(1.03;2.04)

1.40

(0.98; 2.00)

Sex - - <0.001 - - <0.001 <0.001 <0.001

Boys - - 1 - - 1 <0.001 1 1 1

Girls - - 0.46

(0.37; 0.59) - -

0.45

(0.35;0.56)

0.42

(0.32; 0.54)

0.42

(0.33;0.54)

0.42

(0.33; 0.54)

BMI - - 1.09

(1.05; 1.14) <0.001 - -

1.08

(1.04;1.13) <0.001

1.09

(1.04; 1.14) <0.001

1.09

(1.04;1.14) <0.001

1.09

(1.04; 1.15) <0.001

Migration background - - 0.035 - - 0.223 0.267 0.293 0.262

yes - - 1 - - 1 1 1 1

no - - 0.75

(0.57; 0.98) - -

0.85

(0.65;1.11)

0.85

(0.63; 1.14)

0.86

(0.64;1.15)

0.85

(0.63; 1.13)

Physical activity (PA)

(WHO criteria fulfilled) - - 0.738 - - 0.189 0.510 0.404 0.297

Yes - - 1 - - 1 1 1 1

No - - 1.06

(0.75; 1.50) - -

1.25

(0.90;1.74)

1.14

(0.78; 1.66)

1.18

(0.80;1.72)

1.23

(0.84; 1.80)

School type - - - - - - - - 0.036 0.016

High School - - - - - - - - 1 1


School - - - - - - - -

1.49

(1.03;2.15)

1.57

(1.09; 2.27)

Schools’

neighbourhood SES - - - - - - - - - - 0.535

High (rank 1-2) - - - - - - - - - - 1

Middle (rank 3-4) - - - - - - - - - - 1.26

(0.82; 1.92)

Low (rank 5-7) - - - - - - - - - - 1.07

(0.67; 1.70)

*Model 1 (Model 1a: individual socioeconomic status (SES) (FAS), Model 1b: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, PA)

**Model 2 (Model 2a: student’s neighbourhood SES (3 categories); Model 2b: student’s neighbourhood SES (3 categories) adjusted for Sex, BMI, migration background, PA)

***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+ School type, Model 3b: Model 1+2+ School type + School neighbourhood SES)

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abstract



Introduction


reported


Methods








cases and controls









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Data Sources/

Measurement









confounding





addressed


sampling strategy


Results











time




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Discussion







Other Information







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Association of individual and neighbourhood socioeconomic status with physical activity and screen time in 7th grade

boys and girls in Berlin, Germany – a cross sectional study.

Journal: BMJ Open



Date Submitted by the Author: 04-Oct-2017

Complete List of Authors: Krist, Lilian; Charité University Medical Center, Institute for Social Medicine, Epidemiology and Health Economics Bürger, Christin; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Ströbele-Benschop, Nanette; University of Hohenheim, Institute of Nutritional Medicine Roll, Stephanie; Charité Universitätsmedizin, Institute for Social Medicine, Epidemiology and Health Economics Lotz, Fabian; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Rieckmann, Nina; Charité Universitätsmedizin, Berlin School of Public Health Muller-Nordhorn, Jacqueline; Universitätsmedizin Berlin, Berlin School of Public Health Willich, Stefan; Charité University Medical Center Müller-Riemenschneider, Falk; National University of Singapore, Saw Swee HOck School of Public Health; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics


Public health






jopen.bmj.com

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ecember 2017. D

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physical activity and screen time in 7th grade boys and girls in Berlin, Ger-

many – a cross sectional study.



2, Stephanie Roll

1, Fabian Lotz

1,



1, Falk Müller-

Riemenschneider,1,4







Dr. Lilian Krist Institute for Social Medicine, Epidemiology and Health Economics Charité - Universitätsmedizin Berlin Luisenstr. 57 10117 Berlin, Germany E-Mail: [email protected] Tel: +49 30 450 529 109

Word count: 4088


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ABSTRACT


health behaviours in youths in Germany. Our aim was to investigate the association of individual and

neighbourhood SES with physical activity (PA) and screen time (ST) in 12-13 year old students in

Berlin.




Main outcome measures Sociodemographics, anthropometric data and health behaviours were as-


bourhood SES with daily PA and ST. Students’ characteristics were described with means or percent-

ages. Comparisons were performed using Generalized Linear Mixed Model yielding odds ratios (OR)

with 95% confidence intervals.

Results Mean (±SD) age was 12.5±0.5 years, 50.5% were girls, and 34.1% had a migrant background.

Individual SES was only associated with ST. The OR of engaging in more than two hours of ST per

day was 1.88 [1.12;3.14] for students with low compared to high SES. Neighbourhood SES was asso-

ciated with both PA (OR 1.51 [0.93; 2.46]) and ST (OR 1.40 [0.98; 2.00]), for students with low com-

pared to high neighbourhood SES, when adjusting for individual covariates. Additional adjustment for

school type and schools neighbourhood SES attenuated the associations somewhat.

Conclusions Lower individual SES was only associated with higher ST but not with PA, whereas

lower neighbourhood SES was associated with higher PA and higher ST. After consideration of the

school environment (school type and schools’ neighbourhood SES) the effect of neighbourhood SES

on PA and ST was attenuated somewhat, suggesting an important role in the complex relationship

between individual SES, neighbourhood SES and school environment. Further research is warranted to

unravel these relationships and to develop more targeted health promotion strategies in the future.

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socioeconomic status with PA and ST among 7th grade boys and girls attending secondary schools

in Berlin, Germany.

• The study comprises a large sample with students recruited from all 12 districts of Berlin, includ-

ing a variety of neighbourhoods with different levels of socioeconomic status.

• Anthropometric data and PA were not assessed objectively but via self-report.

• Only ST was assessed, while other kinds of sedentary behaviours were not taken into account.

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INTRODUCTION

Physical activity as well as sedentary behaviour have an important impact on health and wellbeing(1).

Low levels of PA are associated with higher health risks already among children and adolescents(2)

and an increasing number of studies have identified sedentary behaviour as an independent risk factor

for diseases such as diabetes and obesity in children and adolescents(3).


the rates of children being active appear to be decreasing over time(4–7). In addition, longitudinal

studies have shown a decline in PA and at the same time an increase in sedentary behaviour among

children and adolescents with increasing age(8–10). ST (time spent watching TV or playing games on

the computer or playing video games) is one important aspect of sedentary behaviour, even though it

does not encompass the total time spent being sedentary(11).

While there is evidence of an association of age and sex with PA(12), studies exploring the influence

of socioeconomic status and built and social environment of children show heterogeneous results(13–

16).

A low individual SES is often associated with a higher BMI and more sedentary time, but not always

with low PA(17–19). In addition to individual SES, studies investigating the social environment (i.e.

social support and social networks, socioeconomic position and income inequality, racial discrimina-

tion, social cohesion and social capital) of children found evidence of an association with PA and

diet(20–22). The built environment has also been shown to be associated with PA among children and

youth(23).




through which a lower neighbourhood socioeconomic status may influence PA and sedentary behav-

iour could be reduced municipal services such as recreational facilities and playgrounds, financial

stress or less possibilities to own a gym membership(22). Also a higher crime rate may lead to less

activities outside(26). With regard to these associations between PA, sedentary behaviour and the

neighbourhood SES, study results are heterogeneous ranging from no association to a clear association

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(27–30). Other studies in turn found that the neighbourhood SES was only a positive modifier for the

association of environmental factors with PA and sedentary behaviour(31,32). Knowing more about

independent associations of individual and neighbourhood SES could help to address groups of ado-

lescents in a more targeted way when implementing prevention strategies (e.g. adapting the content of

health promotion strategies to different neighbourhoods).

Our aim was therefore to investigate the association of individual and neighbourhood socioeconomic

status with PA and ST as one important form of sedentary behaviour in a population based sample of

12 to 13 year old boys and girls attending secondary schools in Berlin, Germany.

METHODS





grade students in Berlin(33). Here, we report cross-sectional data regarding PA and ST among the

students at baseline including associations with individual and neighbourhood socioeconomic status.










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Measurements




health behaviours, such as alcohol consumption, nutrition, PA and ST, as well as height and weight. It

took about 30-40 minutes to complete the questionnaire. Our study group has the status of an associat-

ed project of the HBSC.




room among children, who had provided both consent forms.

Outcome measures


PA was assessed using three adapted items of the HBSC questionnaire. The first question read: ‘On

how many days in the past week were you physically active for at least 60 minutes?’ According to the

WHO guidelines, for our primary outcome we defined a student as meeting current guidelines if he or

she was active at least 60 minutes on each of the last seven days (yes/no)(37). The other questions

asked for the number of days and hours of moderate intensity PA per week.

Screen time (ST)

ST was assessed with two questions (also part of the HBSC questionnaire) asking for the time spent

each day watching TV or playing with the Computer. TV time was assessed by asking ‘How many

hours/day do you usually watch television in your free time?’ for weekdays and weekend days sepa-

rately. Computer time (minutes/day) was assessed by asking ‘How many hours/day do you usually

play games on a computer, or use a game console in your leisure time?’. Total ST was computed by

adding up TV and computer time. Using a smartphone or tablet was not assessed. According to the

AAP(38) recommendations we defined more than 2 hours of ST per day as high ST.

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Covariates

Individual level




derweight (<18.5kg/m2), overweight (25kg/m2) and obesity (30kg/m2). According to that definition,

underweight is defined as a BMI <10th percentile, normal weight as a BMI between the 10th and the


≥97th percentile(39,40). According to official definitions a student was defined as having a migration

background if he or she was not born in Germany or if at least one parent was not born in Germany

but moved to Germany after 1949(41).















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School types

In Berlin, two types of secondary schools exist: high schools with the possibility to achieve a high-

school diploma after 12 years, as well as integrated secondary schools (an integration of different

school types) with the possibility to achieve a high-school diploma after 13 years. More often than

high schools, integrated secondary schools are left by the students after the 10th grade with a secondary

school leaving certificate. The academic requirements are higher in high schools than in integrated

secondary schools(45).

Schools’ neighbourhood socioeconomic status

Since the neighbourhood of the school can be different to that of students, we assessed this infor-

mation (analogous to the individual neighbourhood SES) in order to take an additional influencing

factor of the students’ behaviour into account.


All statistical analyses were performed for the 12 and 13 years old students due to the small number of





tests).


mixed model (GLMM) with a logit link function was used for the analysis when comparing groups

(models with random intercept). In general, the random factors ‘school’ and ‘class within school’ (as

nested factor) were included into the models, with either PA or ST as the dependent variable. Results

are presented as odds ratios (OR) and 95%-confidence intervals (CI).

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These models were used to determine the association of several factors. For PA as the dependent vari-

able, sex, migration background, BMI and ST were included into all models, in addition with individ-

ual socioeconomic status (FAS-score) (Model 1) or students’ neighbourhood SES (Model 2) or both

(Model 3a). A final model included the aforementioned plus the two school level variables school type

and schools’ neighbourhood (Model 3b). The same procedure was performed for ST as the dependent

variable, respectively. To be able to compare different models, the analyses were restricted to the

number of students with non-missing data for the model with the largest number of variables included.

As sensitivity analyses, to assess if associations are modified by gender, interaction effects on gender

were included into the models. Additional sensitivity analyses were performed based on the maximum

number of students with non-missing data for the respective model. All p-values are considered ex-

ploratory (with no adjustment for multiple testing). Analyses were performed using the software pack-

age SAS release 9.3 and 9.4 (SAS Institute Inc., Cary, NC, USA).

RESULTS





2586 students aged 12 and 13 years in our analysis. Figure 1 shows the recruitment process of the

schools, classes and students.

Sociodemographic baseline characteristics of all participating students are presented in Table 1a. The

mean (±SD) age of participants was 12.4 ± 0.5 years (12.5 ± 0.5 for boys and 12.4 ± 0.5 for girls) and

the distribution between girls and boys was similar (50.5% vs. 49.5%). Of the entire sample, 34.1%

were defined as having a migrant background. Boys reported more often a high individual SES than

girls (53.7% vs. 46.3%). School characteristics of are presented in Table 1b. An association between

the students’ neighbourhood SES and the school type could be observed, indicating that the mean stu-

dents’ neighbourhood SES was higher among high school students than integrated secondary school

students.

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girls, OR 1.7 [1.4;2.2]; p<0.001) and boys were more active than girls (0.9±0.8 versus 0.6±0.6 hours

per day, mean difference 0.3 hours [0.2;0.3], p<0.001). More than 80% of the boys and almost two

thirds of the girls reported more than 2 hours ST per day, OR 2.2 [1.8;2.6]; p<0.001. ST on weekend

days was higher than on week days among all students (5.7±3.6 versus 3.5±2.7) (Table 1c).




Number of students, n (%) 1279 (49.5) 1307 (50.5) 2586 Age (years, mean ± SD) (n=2586) 12.5 ± 0.5 12.4 ± 0.5 12.4 ± 0.5 <0.001

12 years, n (%) 651 (50.9) 775 (59.3) 1426 (55.1) <0.001

13 years, n (%) 628 (49.1) 532 (40.7) 1160 (44.9) Height (cm, mean ± SD) (n=2440) 161.1±9.5 160.1±7.3 160.6 ± 8.4 0.003

Weight (kg, mean ± SD) (n=2360) 49.5 ± 10.9 47.0 ± 8.9 48.3 ± 10.0 <0.001 BMI* (kg/m2, mean ± SD) (n=2296) 18.9 ± 3.1 18.3 ± 2.7 18.6 ± 2.9 <0.001

BMI range 11.8 – 30.9 11.7 – 33.8 11.7 – 33.8 Underweight (BMI <10th percentile)** 126 (11.1) 218 (18.8) 344 (15.0) <0.001

Normal weight (BMI 10th - <90th per-centile)**

822 (72.2) 843 (72.6) 1665 (72.4)

Overweight (BMI 90th - <97th percen-tile)**

166 (14.6) 90 (7.8) 256 (11.1)


Migrant background (n=2423) 396 (33.1) 429 (35.0) 825 (34.0) 0.307 Individual SES*** (family affluence scale; FAS) (n=2139)

high (FAS 6-7) 569 (53.7) 500 (46.3) 1069 (50.0) 0.003

moderate (FAS 4-5) 371 (35.0) 441 (40.9) 812 (38.0) low (FAS 0-3) 120 (11.3) 138 (12.8) 258 (12.1)

Students’ neighbourhood SES (n=2240) 1114 1126 2240

Mean±SD 4.0 ± 1.9 4.1 ± 1.9 4.0 ± 1.9 0.526

1 (best) 127 (11.4) 123 (10.9) 250 (11.2)

2 182 (16.3) 194 (17.2) 376 (16.8)

3 143 (12.8) 119 (10.6) 262 (11.7)

4 215 (19.3) 229 (20.3) 444 (19.8) 5 162 (14.5 ) 160 (14.2) 322 (14.4)

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(descriptive statistical methods) *Body Mass Index (kg/m2) ** BMI percentiles according to Cole et al. (46,47) *** Socioeconomic Status a High schools (5th or 7th grade to 12th grade, graduation with high school diploma after 12th grade) b Integrated secondary schools (integration of different school types, 7th grade to 13th grade, graduation with secondary school leaving certificate after 10th grade or high school diploma after 13th grade)

6 134 (12.0) 134 (11.9) 268 (12.0)

7 (worst) 151 (13.6) 167 (14.8) 318 (14.2) School type (n=2586)

High Schoola students (15 schools) 507 (39.6) 624 (47.7) 1131 (43.7) <0.001 Integrated Secondary Schoolb students (32 schools)

772 (60.4) 683 (52.3) 1455 (56.3)

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Table 1c. Comparison of boys and girls aged 12-13 years

Boys

Girls

p-value (boys vs. girls)

Total

Proportion Proportion Odds ratio [95% CI]

Proportion

WHO recommendations fulfilled: 60min/day every day per week (%) (n=2517)

15.9 9.8 1.7 (1.4;2.2) <0.001 12.8

Screen time of 2 hours or more per day (%) (n=2503)

81.5 66.9 2.2 (1.8;2.6) <0.001 74.1

Mean±SD Mean±SD Mean difference

[95% CI] p-value Mean±SD

School level High Schoolsa Integrated Secondary

Schoolsb Total

Schools’ neighbourhood SES, Mean±SD (n=47)

3.5±1.4 4.4±1.9 4.0±1.8 <0.001

n (%)

1 (best) 1 (6.7) 3 (9.4) 4 (8.5)

2 4 (26.7) 3 (9.4) 7 (14.9) 3 2 (13.3) 4 (12.5) 6 (12.8)

4 4 (26.7) 7 (21.9) 11 (23.4) 5 2 (13.3) 4 (12.5) 6 (12.8)

6 2 (13.3) 5 (15.6) 6 (12.8)

7 (worst) 0 (0.0) 6 (18.8) 6 (12.8) Students’ neighbourhood SES*, Mean±SD (n=2240)

3.1±1.6 4.6±1.9 <0.001

Individual SES* (family affluence scale; FAS) (n=2139)

n (%) High (FAS 6-7) 744 (65.8) 531 (36.5)

<0.001 Moderate (FAS 4-5) 338 (29.9) 652 (44.8) Low (FAS 0-3) 49 (4.3) 272 (18.7)

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Time spent with physical activity per day (hours, mean±SD) (n=2517)

0.9±0.8 0.6±0.6 0.3 (0.2;0.3) <0.001 0.8±0.7

Screen time (TV) (hours, mean±SD)

school day (n=2555) 2.2 ± 1.8 1.9 ± 1.6 0.3 (0.2;0.4) <0.001 2.0 ± 1.7

weekend day (n=2550) 3.4 ± 2.0 2.9 ± 2.0 0.4 (0.3;0.6) <0.001 3.1 ± 2.0

Screen time (Computer) (hours, mean±SD) school day (n=2558) 1.8 ± 1.8 1.3 ± 1.6 0.5 (0.4;0.7) <0.001 1.5 ± 1.7

weekend day (n=2541) 3.1 ± 2.2 2.0 ± 2.0 1.1 (1.0;1.3) <0.001 2.6 ± 2.2

Overall screen time (hours, mean±SD) (n=)

school day (n=2536) 3.9 ± 2.7 3.1 ± 2.5 0.8 (0.6;0.97) <0.001 3.5 ± 2.7

weekend day (n=2518) 6.5 ± 3.6 4.9 ± 3.4 1.6 (1.3;1.8) <0.001 5.7 ± 3.6

(descriptive statistical methods)

SD: Standard deviation, CI: Confidence interval


Results of multivariable analyses are presented in figure 2 and figure 3. Results presented in figures 2

and 3 and supplemental tables 1 and 2 are based on an identical analysis population with complete

information (n=1523). Results for the multivariable analysis not restricted to complete cases is pre-

sented in supplement tables 3 and 4. The results did not differ markedly between both approaches.

Individual SES was not associated with PA, but with ST. The lower the students’ SES the higher the

odds to spent more than two hours of ST per day (1.31 [1.00; 1.72] and 2.08 [1.26; 3.43]; p=0.008) for

middle and low individual SES, respectively, compared to high SES). This association was attenuated

slightly when additionally adjusting for school type and school neighbourhood SES (1.25 [0.95;1.64]

and 1.88 [1.12;3.14]; p=0.036).

In contrast to individual SES, a lower neighbourhood SES was associated with a higher odds of engag-

ing in 60 minutes per day in PA (1.34 [0.86;2.08] and 1.76 [1.12; 2.75]) for middle and low neigh-

bourhood SES, respectively, compared to high neighbourhood SES; this association was attenuated

somewhat when additionally adjusting for the school type and schools’ neighbourhood SES.

Compared with high neighbourhood SES, students with lower neighbourhood SES were also more

likely to spend more than two hours of ST per day. The effect was stronger for low than for middle

neighbourhood SES (1.54 [1.10; 2.17] and 1.03 [0.75; 1.41]), and remained largely consistent when

additionally adjusting for school type and school neighbourhood SES (1.40 [0.98; 2.00] and 1.37

[0.99; 1.91]).

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There was no interaction effect between gender and ST regarding PA, nor between gender and PA

regarding ST (data not shown).

DISCUSSION

In this study we investigated the association of individual and neighbourhood SES with PA and ST

among 7th grade school students. The individual SES of the students in our study sample, measured

with the family affluence scale, was significantly associated with ST. Students with lower SES were

more likely to spend more than 2 hours per day viewing screen devices. Compared to high SES, low

SES was more strongly associated with ST than middle SES. Similar results were found in other stud-

ies(16,48,49). Potential reasons for these findings are that parents with better education and higher

statuses may be more aware of the health consequences of excessive ST and thus have stricter rules

regarding ST behaviour(50). Children from families with lower socioeconomic status may also more

often have a TV in their room, which has been shown to be associated with higher ST levels(51).

Moreover, it is well known that parents have an important role-modelling function, which influences

children’s behaviours, such as screen viewing(52). Since children of families with lower SES may

more often have parents that engage in higher ST and/or watch more often TV together with their par-

ents, they may in turn engage in more ST(53).

PA on the other hand was not associated with individual SES in our study population. This finding is

in part consistent with the results of the HBSC study for Germany and with a few other

studies(8,54,55). A possible explanation for this finding is that PA consists not only of organised

sports or activities that require a club membership or sports equipment. On the contrary, a large part of

PA among youths may be daily life activities, such as active commuting, or sports and activities in the

neighbourhood and in parks which is independent from the individual SES(56). However, in contrast

to our findings and the other studies, a variety of studies do show an association between socioeco-

nomic status and PA, which has been highlighted in reviews by Sallis et al or Hanson et al(57,58). It

should be noted that most of these studies are from the US or Australia and the explications for the

observed associations, such as the higher prevalence of unsafe neighbourhoods or of neighbourhoods

with less green space may not be directly transferable to Germany and Berlin.

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The other main aspect of our study was the investigation of the neighbourhood SES and its association

with PA and ST. The neighbourhood SES represents the social and health indicators of a city or of its

districts including unemployment rate, welfare reception rate, average per capita income and others. In

our study, students living in low SES neighbourhood areas were more likely to be physically active

than those with middle or high neighbourhood SES. To a certain extent this is surprising and in con-

trast to many earlier studies that have reported mostly no or inverse associations between neighbour-

hood SES and PA(28,59–62). However, as suggested in an earlier study the observed finding in our

study may be related to higher active transportation among adolescents of families with lower SES

because they may be less likely to own a car resulting in more students using the bicycle or public

transportation to school(63). Similar to individual SES, another explication could be that the major

part of PA among adolescents consists of leisure or unstructured activities rather than organised team

sports(56). Thus, a membership in a sports club (which is less probable in neighbourhoods with lower

SES) would not affect the overall amount of PA.

Low neighbourhood SES was also associated with higher ST compared to high neighbourhood SES.

This result is in line with a study by Carson et al(64). Neighbourhood safety, as suggested by Carson

et al, may be one possible explanation for this finding. In addition, the lack of suitable and well-

maintained recreation facilities could lead to more ST as replacement of other leisure time activities.

In contrast to our results, many studies investigating neighbourhood SES and its association with sed-

entary behaviour reported null results as shown in a recent review by Stierlin et al., suggesting that

other factors may be more important than neighbourhood SES in the context of adolescents’ sedentary

behaviour(30). Possible reasons for these differences between findings may be related to different

study populations across individual studies but also the fact that our study only focussed on ST instead

of total sedentary behaviour. Screen viewing as a health behaviour has not been investigated widely in

the context of individual and neighbourhood SES, but it appears that in Berlin it is more closely linked

with these factors than PA. Hence, promoting alternative activity opportunities for adolescents living

in lower SES neighbourhoods could be a worthwhile target for interventions. In the context of the

existing literature it would be useful to also investigate total sedentary behaviour in future German

studies.

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In addition to individual and neighbourhood SES, school level factors play a role in the health behav-

iour of school children(65). Moore et al found that school level affluence was independently associat-

ed with health behaviours (except physical activity) of the school students after adjusting for the indi-

vidual SES(66). When additionally including school type and school neighbourhood SES as covariates

in our analysis, presented results for PA and ST were attenuated somewhat, indicating the potentially

important role of school type and school neighbourhood SES on PA and ST. A possible explanation

for this finding could be that adolescents living in areas with lower neighbourhood SES are more often

attending an integrated secondary school. Since the academic standards of integrated secondary

schools tend to be lower than those of high schools, it is possible that students of the first-mentioned

have more leisure time than those of the latter(67,68).

Some studies found that the school socioeconomic environment i.e. social networks and peer influ-

ences had a greater effect on health behaviour among adolescents than the individual SES(66,69). This

illustrates the complex interplay of individual SES, neighbourhood SES, and the school environment

(school type and school neighbourhood SES), that may also be affected by parental choice of schools

and other parental influences on school activities(70). A recent study from the UK has provided some

further evidence for these complex relationships(66). Studies from Germany have also shown that the

neighbourhood SES as well as the SES of the students tends to be correlated with the school type(71).

Better educated parents tend to send their children to high schools rather than integrated secondary

schools(72), which could imply that it is not only the school type itself influencing PA and ST, but the

social environment of the student. But even if the choice of the school type is done by the parents and

is influenced by their SES, targeting integrated secondary schools may be important and could be em-

phasized more in health promotion activities. It appears that this may help to address the issue of indi-

vidual SES on the one hand (more children with low SES in secondary schools) but also neighbour-

hood SES on the other hand. Further research is needed to disentangle these complex relationships

between individual and neighbourhood SES, as well as school environment. With additional research

it could be investigated if some neighbourhoods might benefit more from screen time related activi-

ties, while others might benefit more from PA related activities. The aim should be the ability to target

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the content of health promotion activities according to school type and neighbourhood to meet greatest

needs.

In addition to individual and neighbourhood SES, other factors like the built environment (i.e. number

of public transport stops, residential density, intersection density and the number of parks) could also

play an important role in adolescents’ health behaviours(73). These factors may be mediators of the

observed associations but studies have also suggested that associations may be moderated by the built

environment (studies have shown that individuals with low neighbourhood SES had a greater benefit

of a good walkability than those with a high neighbourhood SES)(31,32). Future research should

therefore also include measurements of the built environment in Berlin to provide new insights into

the associations with PA.





teristics as Berlin: an urban well-connected region with relatively safe neighbourhoods and good infra-

structure for transportation and cycling.


up. However, we assessed one item of the FAS (holiday) additionally at baseline and at the 12 month

follow-up. The answers were quite similar over the two years. We thus think the period of two years

implicates only minimal changes in the FAS level. We also found differences in the self-report FAS of

boys and girls, which is somewhat surprising. It is possible that the structure of the questionnaire led

to an overestimation among boys due to a higher interest in cars and computers (i.e. two key elements

of the FAS). Second, anthropometric data and PA were not measured objectively. Self-report of chil-

dren and adolescents, especially regarding PA, may lead to biased results through misreporting(75).

Measurement errors associated with self-report may further be influenced by SES of adolescents(76).

Future studies should use accelerometers or other means to objectively measure PA and sedentary

behaviour(77). Another limitation of our study is that we did not assess total sedentary behaviour and

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that ST was determined based on the use of TV, Computer and video games as assessed by the HBSC

questionnaire(35,78). Other increasingly popular screen devices (e.g. smartphones, tablets) and other

kinds of sedentary behaviours like sitting during homework, talking on the phone and sitting at school

were not taken into account, which may have led to an underestimation of ST.

CONCLUSION

Lower individual SES was only associated with higher ST but not with PA, whereas lower neighbour-

hood SES was associated with higher PA and higher ST. After consideration of the school environ-

ment (school type and schools neighbourhood SES) the effect of neighbourhood SES on PA and ST

was attenuated somewhat, suggesting an important role in the complex relationship between individual

SES, neighbourhood SES and school environment. Further research is warranted to unravel these rela-

tionships and to develop more targeted health promotion strategies in the future.

ACKNOWLEDGEMENTS



CONTRIBUTORS






FUNDING


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COMPETING INTERESTS

None declared.

ETHICS APPROVEL

The study was approved by the ethical review committee of the Charité-Universitätsmedizin Berlin, Germany.



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Figures



students (complete case analysis, n=1523)


(complete case analysis, n=1523)

Supplementary files


years old students (complete case analysis, n=1523)


old students (complete case analysis, n=1523)


years old students (unequal sample sizes)


old students (unequal sample sizes)

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254x338mm (300 x 300 DPI)

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Multivariable analysis of physical activity associated factors among 12 and 13 years old students (complete case analysis, n=1523)

380x331mm (300 x 300 DPI)

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Multivariable analysis of screen time associated factors among 12 and 13 years old stu-dents (complete case analysis, n=1523)

333x312mm (300 x 300 DPI)

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Model 1* Model 2**

Model 3***

Model 3a Model 3b

OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value

Individual socioeconomic status (FAS) 0.984 - - 0.792 0.476

High 1 - - 1 1

Middle 0.98 (0.69; 1.38) - - 0.90 (0.63;1.29) 0.83 (0.58; 1.20)

Low 0.96 (0.54; 1.70) - - 0.85 (0.48; 1.52) 0.74 (0.41; 1.33)

Students’ neighbourhood SES - - - 0.058 0.047 0.253

High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.31 (0.85;2.04) 1.34 (0.86; 2.08) 1.19 (0.78; 1.82)

Low (rank 5-7) - - 1.70 (1.10;2.63) 1.76 (1.12; 2.75) 1.51 (0.93; 2.46)

Sex <0.001 <0.001 <0.001 <0.001

Boys 1 1 1 1

Girls 0.48 (0.34; 0.67) 0.47 (0.33;0.65) 0.47 (0.34; 0.66) 0.48 (0.34; 0.68)

BMI 0.92 (0.87; 0.98) 0.007 0.92 (0.86;0.98) 0.005 0.92 (0.86; 0.98) 0.006 0.91 (0.86; 0.97) 0.003

Migration background 0.457 0.786 0.716 0.761

yes 1 1 1 1

no 0.87 (0.61; 1.25) 0.95 (0.66;1.37) 0.93 (0.65; 1.35) 0.94 (0.65; 1.37)

Screen time 0.429 0.311 - 0.334 0.233

≥2 hours 1 1 1 1

<2 hours 1.16 (0.81; 1.67) 1.21 (0.84;1.75) 1.20 (0.83; 1.74) 1.26 (0.86; 1.86)

School type - - - - - 0.002

High School - - - - - 1

Integrated Secondary School - - - - - 2.15 (1.34; 3.47)

Schools’ neighbourhood SES - - - - - 0.723

High (rank 1-2) - - - - - 1

Middle (rank 3-4) - - - - - 1.15 (0.69; 1.92)

Low (rank 5-7) - - - - - 0.92 (0.52; 1.62)

Complete case analysis (n=1523) *Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, screen time

**Model 2: students’ neighbourhood SES (3 categories) adjusted for Sex, BMI, migration background, screen time

***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+School type+Schools’ neighbourhood SES

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High screen time (>2 hours per day)

Model 1* Model 2**

Model 3***

Model 3a Model 3b


Individual SES (FAS) 0.002 - - 0.008 0.036

High 1 - - 1 1

Middle 1.36 (1.04; 1.78) - - 1.31 (1.00; 1.72) 1.25 (0.95; 1.64)

Low 2.25 (1.37; 3.68) - - 2.08 (1.26; 3.43) 1.88 (1.12; 3.14)

Students’ neighbourhood SES - - 0.005 0.019 0.109

High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.08 (0.79;1.48) 1.03 (0.75; 1.41) 1.37 (0.99; 1.91)

Low (rank 5-7) - - 1.68 (1.20;2.35) 1.54 (1.10; 2.17) 1.40 (0.98; 2.00)

Sex <0.001 <0.001 <0.001

Boys 1 1 <0.001 1 1

Girls 0.43 (0.33; 0.55) 0.43 (0.33;0.56) 0.42 (0.32; 0.54) 0.42 (0.33; 0.54)

BMI 1.10 (1.05; 1.15) <0.001 1.11 (1.05;1.15) <0.001 1.10 (1.05; 1.15) <0.001 1.09 (1.04; 1.15) <0.001


yes 1 1 1 1

no 0.79 (0.59; 1.05) 0.79 (0.59;1.06) 0.84 (0.63; 1.12) 0.85 (0.63; 1.13)

Physical activity (PA) (WHO criteria

fulfilled) 0.523 0.389 0.430 0.297

Yes 1 1 1 1

No 1.13 (0.78; 1.65) 1.18 (0.81;1.73) 1.17 (0.80; 1.70) 1.23 (0.84; 1.80)

School type - - - - 0.016

High School - - - - 1

Integrated Secondary School - - - - 1.57 (1.09; 2.27)

Schools’ neighbourhood SES - - - - 0.535

High (rank 1-2) - - - - 1

Middle (rank 3-4) - - - - 1.26 (0.82; 1.92)

Low (rank 5-7) - - - - 1.07 (0.67; 1.70)

Complete case analysis (n=1523) *Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, PA

**Model 2: students’ neighbourhood SES (3 categories) adjusted for Sex, BMI, migration background, PA

***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+ School type + Schools’ neighbourhood SES)

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(n=1760) (n=1818) Model 3a (n=1547) Model 3b (n=1523)



High 1 - - 1 1

Middle 0.95 (0.69; 1.31) - - 0.89 (0.62; 1.26) 0.83 (0.58; 1.20)

Low 1.01 (0.60; 1.69) - - 0.87 (0.50; 1.54) 0.74 (0.41; 1.33)


High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.24 (0.84;1.84) 1.27 (0.85; 1.88) 1.19 (0.78; 1.82)

Low (rank 5-7) - - 1.67 (1.14;2.44) 1.65 (1.07; 2.56) 1.51 (0.93; 2.46)

Sex <0.001 <0.001 <0.001 <0.001

Boys 1 1 1 1

Girls 0.51 (0.38; 0.69) 0.48 (0.36;0.65) 0.48 (0.35; 0.67) 0.48 (0.34; 0.68)

BMI 0.90 (0.85; 0.95) <0.001 0.92 (0.87;0.97) 0.002 0.92 (0.87; 0.98) 0.006 0.91 (0.86; 0.97) 0.003


yes 1 1 1 1

no 0.97 (0.69; 1.36) 0.99 (0.72;1.36) 0.91 (0.63; 1.30) 0.94 (0.65; 1.37)

Screen time 0.734 0.165 - 0.425 0.233

≥2 hours 1 1 1 1

<2 hours 1.06 (0.76; 1.49) 1.26 (0.91;1.74) 1.16 (0.81; 1.67) 1.26 (0.86; 1.86)

School type - - - - - 0.002



Schools’ neighbourhood SES - - - - - 0.723

High (rank 1-2) - - - - - 1

Middle (rank 3-4) - - - - - 1.15 (0.69; 1.92)

Low (rank 5-7) - - - - - 0.92 (0.52; 1.62)

*Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, screen time


***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+School type+Schools’neighbourhood SES

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High 1 - - 1 1

Middle 1.28 (1.00; 1.64) - - 1.32 (1.01; 1.73) 1.25 (0.95; 1.64)

Low 2.03 (1.30; 3.15) - - 2.09 (1.27; 3.45) 1.88 (1.12; 3.14)


High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.07 (0.80;1.44) 1.49 (1.08; 2.05) 1.37 (0.99; 1.91)

Low (rank 5-7) - - 1.68 (1.22;2.29) 1.55 (1.10; 2.17) 1.40 (0.98; 2.00)

Sex <0.001 <0.001 <0.001

Boys 1 1 <0.001 1 1

Girls 0.46 (0.37; 0.59) 0.45 (0.35;0.56) 0.42 (0.32; 0.54) 0.42 (0.33; 0.54)

BMI 1.09 (1.05; 1.14) <0.001 1.08 (1.04;1.13) <0.001 1.09 (1.04; 1.14) <0.001 1.09 (1.04; 1.15) <0.001


yes 1 1 1 1

no 0.75 (0.57; 0.98) 0.85 (0.65;1.11) 0.85 (0.63; 1.14) 0.85 (0.63; 1.13)


fulfilled) 0.738 0.189 0.510 0.297

Yes 1 1 1 1

No 1.06 (0.75; 1.50) 1.25 (0.90;1.74) 1.14 (0.78; 1.66) 1.23 (0.84; 1.80)




Schools’ neighbourhood SES - - - - 0.535

High (rank 1-2) - - - - 1

Middle (rank 3-4) - - - - 1.26 (0.82; 1.92)

Low (rank 5-7) - - - - 1.07 (0.67; 1.70)

*Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, PA


***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+ School type + Schools’ neighbourhood SES)

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Page No.


abstract



Introduction


reported


Methods








cases and controls









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Page No.

Data Sources/

Measurement









confounding





addressed


sampling strategy


Results











time




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Discussion







Other Information







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Association of individual and neighbourhood socioeconomic status with physical activity and screen time in 7th grade

boys and girls in Berlin, Germany – a cross sectional study.

Journal: BMJ Open



Date Submitted by the Author: 03-Nov-2017

Complete List of Authors: Krist, Lilian; Charité University Medical Center, Institute for Social Medicine, Epidemiology and Health Economics Bürger, Christin; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Ströbele-Benschop, Nanette; University of Hohenheim, Institute of Nutritional Medicine Roll, Stephanie; Charité Universitätsmedizin, Institute for Social Medicine, Epidemiology and Health Economics Lotz, Fabian; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics Rieckmann, Nina; Charité Universitätsmedizin, Berlin School of Public Health Muller-Nordhorn, Jacqueline; Universitätsmedizin Berlin, Berlin School of Public Health Willich, Stefan; Charité University Medical Center Müller-Riemenschneider, Falk; National University of Singapore, Saw Swee HOck School of Public Health; Charité-Universitätsmedizin Berlin, Institute for Social Medicine, Epidemiology and Health Economics


Public health






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ecember 2017. D

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physical activity and screen time in 7th grade boys and girls in Berlin, Ger-

many – a cross sectional study.



2, Stephanie Roll

1, Fabian Lotz

1,



1, Falk Müller-

Riemenschneider,1,4







Dr. Lilian Krist Institute for Social Medicine, Epidemiology and Health Economics Charité - Universitätsmedizin Berlin Luisenstr. 57 10117 Berlin, Germany E-Mail: [email protected] Tel: +49 30 450 529 109

Word count: 4283


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ABSTRACT


health behaviours in youths in Germany. Our aim was to investigate the association of individual and

neighbourhood SES with physical activity (PA) and screen time (ST) in 12-13 year old students in

Berlin.




Main outcome measures Sociodemographics, anthropometric data and health behaviours were as-


bourhood SES with meeting daily PA and exceeding daily ST recommendations. Students’ character-

istics were described with means or percentages. Comparisons were performed using Generalized

Linear Mixed Model yielding odds ratios (OR) with 95% confidence intervals.

Results Mean(±SD) age was 12.5±0.5 years, 50.5% were girls, and 34.1% had a migrant background.

When adjusting for individual covariates, associations of low versus high individual SES were 0.85

[0.48;1.52] for PA and 2.08 [1.26;3.43] for ST. Associations of low versus high neighbourhood SES

were 1.76 [1.12;2.75] for PA and 1.54 [1.10;2.17] for ST. After additional adjustment for school type

and school neighbourhood SES, associations comparing low versus high individual and neighbour-

hood SES were attenuated for PA (individual SES 0.74 [0.41;1.33] and neighbourhood SES 1.51

[0.93;2.46]) and ST (individual SES 1.88 [1.12;3.14] and neighbourhood SES 1.40 [0.98;2.00].

Conclusions

Lower individual and neighbourhood SES was associated with higher ST. Lower neighbourhood but

not individual SES was associated with higher PA. After consideration of school type and school

neighbourhood SES associations were attenuated and became insignificant for the relationship be-

tween neighbourhood SES, PA and ST. Further research is warranted to unravel the complex relation-

ships between individual SES, neighbourhood SES and school environment to develop more targeted

health promotion strategies in the future.

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socioeconomic status with physical activity and screen time among 7th grade boys and girls attend-

ing secondary schools in Berlin, Germany.

• The study comprises a large sample with students recruited from all 12 districts of Berlin, includ-

ing a variety of neighbourhoods with different levels of socioeconomic status.

• Physical activity was not assessed objectively but via self-report and only ST was assessed, while

other types of sedentary behaviours were not taken into account.

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INTRODUCTION

Physical activity as well as sedentary behaviour have an important impact on health and wellbeing(1).

Low levels of PA are associated with higher health risks already among children and adolescents(2)

and an increasing number of studies have identified sedentary behaviour as an independent risk factor

for diseases such as diabetes and obesity in children and adolescents(3).


the rates of children being active appear to be decreasing over time(4–7). In addition, longitudinal

studies have shown a decline in PA and at the same time an increase in sedentary behaviour among

children and adolescents with increasing age(8–10). ST (time spent watching TV or playing games on

the computer or playing video games) is one important aspect of sedentary behaviour, even though it

does not encompass the total time spent being sedentary(11).

While there is evidence of an association of age and sex with PA(12), studies exploring the influence

of socioeconomic status and built and social environment of children show heterogeneous results(13–

16).

A low individual SES is often associated with a higher BMI and more sedentary time, but not always

with low PA(17–19). In addition to individual SES, studies investigating the social environment (i.e.

social support and social networks, socioeconomic position and income inequality, racial discrimina-

tion, social cohesion and social capital) of children found evidence of an association with PA and

diet(20–22). The built environment has also been shown to be associated with PA among children and

youth(23).




through which a lower neighbourhood socioeconomic status may influence PA and sedentary behav-

iour could be reduced municipal services such as recreational facilities and playgrounds, financial

stress or less possibilities to own a gym membership(22). Also a higher crime rate may lead to less

activities outside(26). With regard to these associations between PA, sedentary behaviour and the

neighbourhood SES, study results are heterogeneous ranging from no association to a clear association

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(27–30). Other studies in turn found that the neighbourhood SES was only a positive modifier for the

association of environmental factors with PA and sedentary behaviour(31,32). Knowing more about

independent associations of individual and neighbourhood SES could help to address groups of ado-

lescents in a more targeted way when implementing prevention strategies (e.g. adapting the content of

health promotion strategies to different neighbourhoods).

Our aim was therefore to investigate the association of individual and neighbourhood socioeconomic

status with PA and ST as one important form of sedentary behaviour in a population based sample of

12 to 13 year old boys and girls attending secondary schools in Berlin, Germany.

METHODS





grade students in Berlin(33). Here, we report cross-sectional data regarding PA and ST among the

students at baseline including associations with individual and neighbourhood socioeconomic status.










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Measurements




health behaviours, such as alcohol consumption, nutrition, PA and ST, as well as height and weight. It

took about 30-40 minutes to complete the questionnaire. Our study group has the status of an associat-

ed project of the HBSC.




room among children, who had provided both consent forms.

Outcome measures


PA was assessed using two adapted items of the HBSC questionnaire. The first question read: ‘On

how many days in the past week were you physically active for at least 60 minutes?’ According to the

WHO guidelines, for our primary outcome we defined a student as meeting current guidelines if he or

she was active at least 60 minutes on each of the last seven days (yes/no)(37). The other question

asked for the number of hours of moderate intensity PA per week (‘How many hours per week are you

physically active (any activity that increases your heart rate and makes you get out of breath)?’) with

examples of such activities. This number was divided by seven to obtain the number of hours of PA

per day.

Screen time (ST)

ST was assessed with two questions (also part of the HBSC questionnaire) asking for the time spent

each day watching TV or playing with the Computer. TV time was assessed by asking ‘How many

hours/day do you usually watch television in your free time?’ for weekdays and weekend days sepa-

rately. Computer time (minutes/day) was assessed by asking ‘How many hours/day do you usually

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play games on a computer, or use a game console in your leisure time?’. Total ST was computed by

adding up TV and computer time. Using a smartphone or tablet was not assessed. According to the

AAP(38) recommendations we defined more than 2 hours of ST per day as high ST.

Covariates

Individual level




derweight (<18.5kg/m2), overweight (25kg/m2) and obesity (30kg/m2). According to that definition,

underweight is defined as a BMI <10th percentile, normal weight as a BMI between the 10th and the


≥97th percentile(39,40).

According to official definitions a student was defined as having a migration background if he or she

was not born in Germany or if at least one parent was not born in Germany but moved to Germany

after 1949(41).









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School types

In Berlin, two types of secondary schools exist: high schools with the possibility to achieve a high-

school diploma after 12 years, as well as integrated secondary schools (an integration of different

school types) with the possibility to achieve a high-school diploma after 13 years. More often than

high schools, integrated secondary schools are left by the students after the 10th grade with a secondary

school leaving certificate. The academic requirements are higher in high schools than in integrated

secondary schools(45).

School neighbourhood socioeconomic status

Since the neighbourhood of the school can be different to that of students, we assessed this infor-

mation (analogous to the individual neighbourhood SES) in order to take an additional influencing

factor of the students’ behaviour into account.


All statistical analyses were performed for the 12 and 13 year old students due to the small number of





tests).

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mixed model (GLMM) with a logit link function was used for the analysis when comparing groups

(models with random intercept). In general, the random factors ‘school’ and ‘class within school’ (as

nested factor) were included into the models, with either PA or ST as the dependent variable. Results

are presented as odds ratios (OR) and 95%-confidence intervals (CI).

These models were used to determine the association of several factors. For PA as the dependent vari-

able, sex, migration background, BMI and ST were included into all models, in addition with individ-

ual socioeconomic status (FAS-score) (Model 1) or students’ neighbourhood SES (Model 2) or both

(Model 3a). A final model included the aforementioned plus the two school level variables school type

and school neighbourhood (Model 3b). The same procedure was performed for ST as the dependent

variable, respectively. To be able to compare different models, the analyses were restricted to the

number of students with non-missing data for the model with the largest number of variables included.

As sensitivity analyses, to assess if associations are modified by gender, interaction effects on gender

were included into the models. Additional sensitivity analyses were performed based on the maximum

number of students with non-missing data for the respective model. All p-values are considered ex-

ploratory (with no adjustment for multiple testing). Analyses were performed using the software pack-

age SAS release 9.3 and 9.4 (SAS Institute Inc., Cary, NC, USA).

RESULTS





2586 students aged 12 and 13 years in our descriptive analyses and 1523 in our complete case anal-

yses. Figure 1 shows the recruitment process of the schools, classes and students.

Sociodemographic characteristics of all participating students are presented in Table 1a. The mean

(±SD) age of participants was 12.4 ± 0.5 years (12.5 ± 0.5 for boys and 12.4 ± 0.5 for girls) and the

distribution between girls and boys was similar (50.5% vs. 49.5%). Of the entire sample, 34.1% were

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defined as having a migrant background. Boys reported more often a high individual SES than girls

(53.7% vs. 46.3%). Mean neighbourhood SES was similar among boys and girls (4.0±1.9 and 4.1±

1.9). Individual and neighbourhood SES were moderately correlated (spearman’s rank correlation

coefficient =0.36; p<0.001). School characteristics are presented in Table 1b. An association between

the students’ neighbourhood SES and the school type could be observed, indicating that the mean stu-

dents’ neighbourhood SES was higher among high school students than integrated secondary school

students.

Of the total sample, 12.8% fulfilled the WHO criteria of being active for at least 60 minutes per day.

The proportion of boys fulfilling the criteria was higher than in girls (15.9% of the boys vs. 9.8% of

the girls, OR 1.7 [1.4;2.2]; p<0.001) and boys also spent more time being active than girls (0.9±0.8

versus 0.6±0.6 hours per day, mean difference 0.3 hours [0.2;0.3], p<0.001). 81.5% of the boys and

66.9% of the girls reported more than 2 hours ST per day, OR 2.2 [1.8;2.6]; p<0.001. Average ST was

also higher among boys than among girls (3.9±2.7 hours vs. 3.1±2.5 hours; p<0.001 on week days and

6.5±3.6 hours vs. 4.9±3.4 hours on weekend days.

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(descriptive statistical methods) *Body Mass Index (kg/m2) ** BMI percentiles according to Cole et al. (46,47) *** Socioeconomic Status a High schools (5th or 7th grade to 12th grade, graduation with high school diploma after 12th grade) b Integrated secondary schools (integration of different school types, 7th grade to 13th grade, graduation with secondary school leaving certificate after 10th grade or high school diploma after 13th grade)




Age (years, mean ± SD) (n=2586) 12.5 ± 0.5 12.4 ± 0.5 12.4 ± 0.5 <0.001 12 years, n (%) 651 (50.9) 775 (59.3) 1426 (55.1) <0.001

13 years, n (%) 628 (49.1) 532 (40.7) 1160 (44.9) Height (cm, mean ± SD) (n=2440) 161.1±9.5 160.1±7.3 160.6 ± 8.4 0.003

Weight (kg, mean ± SD) (n=2360) 49.5 ± 10.9 47.0 ± 8.9 48.3 ± 10.0 <0.001 BMI* (kg/m2, mean ± SD) (n=2296) 18.9 ± 3.1 18.3 ± 2.7 18.6 ± 2.9 <0.001

BMI range 11.8 – 30.9 11.7 – 33.8 11.7 – 33.8

Underweight (BMI <10th percentile)** 126 (11.1) 218 (18.8) 344 (15.0) <0.001

Normal weight (BMI 10th - <90th per-centile)**

822 (72.2) 843 (72.6) 1665 (72.4)

Overweight (BMI 90th - <97th percen-tile)**

166 (14.6) 90 (7.8) 256 (11.1)


Migrant background (n=2423) 396 (33.1) 429 (35.0) 825 (34.0) 0.307 Individual SES*** (family affluence scale; FAS) (n=2139)

high (FAS 6-7) 569 (53.7) 500 (46.3) 1069 (50.0) 0.003

moderate (FAS 4-5) 371 (35.0) 441 (40.9) 812 (38.0) low (FAS 0-3) 120 (11.3) 138 (12.8) 258 (12.1)

Students’ neighbourhood SES (n=2240) 1114 1126 2240

Mean±SD 4.0 ± 1.9 4.1 ± 1.9 4.0 ± 1.9 0.526 1 (best) 127 (11.4) 123 (10.9) 250 (11.2)

2 182 (16.3) 194 (17.2) 376 (16.8) 3 143 (12.8) 119 (10.6) 262 (11.7)

4 215 (19.3) 229 (20.3) 444 (19.8) 5 162 (14.5 ) 160 (14.2) 322 (14.4)

6 134 (12.0) 134 (11.9) 268 (12.0)

7 (worst) 151 (13.6) 167 (14.8) 318 (14.2)



Integrated Secondary Schoolb students (32 schools)

772 (60.4) 683 (52.3) 1455 (56.3)

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Results of multivariable analyses are presented in figure 2 and figure 3. These results presented in

figures 2 and 3 and in supplement table1 and supplement table 2 are based on an identical analysis

population with complete information (n=1523). Results for the multivariable analysis not restricted to

complete cases is additionally presented in supplement table 3 and supplement table 4. The results did

not differ markedly between both approaches.

School level High Schoolsa Integrated Secondary

Schoolsb Total

School neighbourhood SES, Mean±SD (n=47)

3.5±1.4 4.4±1.9 4.0±1.8 <0.001

n (%)

1 (best) 1 (6.7) 3 (9.4) 4 (8.5)

2 4 (26.7) 3 (9.4) 7 (14.9) 3 2 (13.3) 4 (12.5) 6 (12.8)

4 4 (26.7) 7 (21.9) 11 (23.4) 5 2 (13.3) 4 (12.5) 6 (12.8)

6 2 (13.3) 5 (15.6) 6 (12.8)

7 (worst) 0 (0.0) 6 (18.8) 6 (12.8) Students’ neighbourhood SES*, Mean±SD (n=2240)

3.1±1.6 4.6±1.9 <0.001

Individual SES* (family affluence scale; FAS) (n=2139)

n (%) High (FAS 6-7) 744 (65.8) 531 (36.5)

<0.001 Moderate (FAS 4-5) 338 (29.9) 652 (44.8) Low (FAS 0-3) 49 (4.3) 272 (18.7)

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In multivariable analyses individual SES was not associated with PA. The ORs after adjustment for

individual factors were 0.90 [0.63;1.29] and 0.85 [0.48;1.52]; p=0.792 for middle and low SES, re-

spectively, compared to high SES. Additional adjustment for school type and school neighbourhood

SES did not change the results notably (0.83 [0.58;1.20] and 0.74 [0.41;1.33]; p=0.476). ST in contrast

was associated with individual SES. The lower the students’ SES the higher the odds to spent more

than two hours of ST per day (1.31 [1.00;1.72] and 2.08 [1.26;3.43]; p=0.008) for middle and low

individual SES, respectively, compared to high SES. This association was attenuated slightly when

additionally adjusting for school variables (1.25 [0.95;1.64] and 1.88 [1.12;3.14]; p=0.036).

In contrast to individual SES, a lower neighbourhood SES was associated with a higher odds of engag-

ing in 60 minutes per day in PA (1.34 [0.86;2.08] and 1.76 [1.12;2.75]; p=0.047) for middle and low

neighbourhood SES, respectively, compared to high neighbourhood SES after adjustment for individ-

ual factors; after adjustment for school variables, the association of neighbourhood SES with PA was

attenuated somewhat and no longer independently associated (OR 1.19 [0.78;1.82] and 1.51

[0.93;2.46]; p=0.253).

Compared with high neighbourhood SES, students with low neighbourhood SES were more likely to

spend more than two hours of ST per day (OR 1.54 [1.10;2.17]), while there was no association for

students with middle neighbourhood SES (1.03 [0.75;1.41]; p=0.019). When additionally adjusting for

school variables, neighbourhood SES was no longer independently associated with ST and the OR of

middle and low neighbourhood SES, compared to high neighbourhood SES, became almost equal

(1.37 [0.99;1.91] and 1.40 [0.98;2.00]; p=0.109). There was no interaction effect between gender and

ST regarding PA, nor between gender and PA regarding ST (data not shown).

DISCUSSION

In this study we investigated the association of individual and neighbourhood SES with PA and ST

among 7th grade school students. The individual SES of the students in our study sample, measured

with the family affluence scale, was significantly associated with ST. Students with lower SES were

more likely to spend more than 2 hours per day viewing screen devices. Compared to high SES, low

SES was more strongly associated with ST than middle SES. Similar results were found in other stud-

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ies(16,48,49). Potential reasons for these findings are that parents with better education and higher

statuses may be more aware of the health consequences of excessive ST and thus have stricter rules

regarding ST behaviour(50). Children from families with lower socioeconomic status may also more

often have a TV in their room, which has been shown to be associated with higher ST levels(51).

Moreover, it is well known that parents have an important role-modelling function, which influences

children’s behaviours, such as screen viewing(52). Since children of families with lower SES may

more often have parents that engage in higher ST and/or watch more often TV together with their par-

ents, they may in turn engage in more ST(53).

PA on the other hand was not associated with individual SES in our study population. This finding is

in part consistent with the results of the HBSC study for Germany and with a few other

studies(8,54,55). A possible explanation for this finding is that PA consists not only of organised

sports or activities that require a club membership or sports equipment. On the contrary, a large part of

PA among youths may be daily life activities, such as active commuting, or sports and activities in the

neighbourhood and in parks which is independent from the individual SES(56). However, in contrast

to our findings and the other studies, a variety of studies do show an association between socioeco-

nomic status and PA, which has been highlighted in reviews by Sallis et al or Hanson et al(57,58). It

should be noted that most of these studies are from the US or Australia and the explanations for the

observed associations, such as the higher prevalence of unsafe neighbourhoods or of neighbourhoods

with less green space may not be directly transferable to Germany and Berlin.

The other main aspect of our study was the investigation of the neighbourhood SES and its association

with PA and ST. The neighbourhood SES represents the social and health indicators of a city or of its

districts including unemployment rate, welfare reception rate, average per capita income and others. In

our study, students living in low SES neighbourhood areas were more likely to be physically active

than those with middle or high neighbourhood SES. To a certain extent this is surprising and in con-

trast to many earlier studies that have reported mostly no or inverse associations between neighbour-

hood SES and PA(28,59–62). However, as suggested in an earlier study the observed finding in our

study may be related to higher active transportation among adolescents of families with lower SES

because they may be less likely to own a car resulting in more students using the bicycle or public

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transportation to school(63). Similar to individual SES, another explication could be that the major

part of PA among adolescents consists of unstructured activities rather than organised team sports(56).

Thus, a membership in a sports club (which is less probable in neighbourhoods with lower SES)

would not affect the overall amount of PA.

Low and middle neighbourhood SES were also associated with higher ST compared to high neigh-

bourhood SES. This result is in line with a study by Carson et al(64). Neighbourhood safety, as sug-

gested by Carson et al, may be one possible explanation for this finding. In addition, the lack of suita-

ble and well-maintained recreation facilities could lead to more ST as replacement of other leisure

time activities. In contrast to our results, many studies investigating neighbourhood SES and its asso-

ciation with sedentary behaviour reported null results as shown in a recent review by Stierlin et al.,

suggesting that other factors may be more important than neighbourhood SES in the context of adoles-

cents’ sedentary behaviour(30). Possible reasons for these differences between findings may be related

to different study populations across individual studies but also the fact that our study only focussed

on ST instead of total sedentary behaviour. Screen viewing as a health behaviour has not been investi-

gated widely in the context of individual and neighbourhood SES, but it appears that in Berlin it is

more closely linked with these factors than PA. Hence, promoting alternative activity opportunities for

adolescents living in lower SES neighbourhoods could be a worthwhile target for interventions. In the

context of the existing literature, it would be useful to also investigate total sedentary behaviour in

future German studies.

In addition to individual and neighbourhood SES, school level factors play a role in the health behav-

iour of school children(65). Moore et al found that school level affluence was independently associat-

ed with health behaviours (except physical activity) of the school students after adjusting for the indi-

vidual SES(66). When additionally including school type and school neighbourhood SES as covariates

in our analysis, presented results for PA and ST were attenuated somewhat and neighbourhood SES

was no longer independently associated with PA and ST, indicating the potentially important role of

school type and school neighbourhood SES on PA and ST.

A possible explanation for this finding could be that adolescents living in areas with lower neighbour-

hood SES are more often attending an integrated secondary school which has been shown in table 1b.

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Since the academic standards of integrated secondary schools tend to be lower than those of high

schools, it is possible that students of the first-mentioned have more leisure time than those of the lat-

ter(67,68).

Some studies found that the school socioeconomic environment i.e. social networks and peer influ-

ences had a greater effect on health behaviour among adolescents than the individual SES(66,69). This

illustrates the complex interplay of individual SES, neighbourhood SES, and the school environment

(school type and school neighbourhood SES), that may also be affected by parental choice of schools

and other parental influences on school activities(70). A recent study from the UK has provided some

further evidence for these complex relationships(66). Studies from Germany have also shown that the

neighbourhood SES as well as the SES of the students tends to be correlated with the school type(71).

Better educated parents tend to send their children to high schools rather than integrated secondary

schools(72), which could imply that it is not only the school type itself influencing PA and ST, but the

social environment of the student. But even if the choice of the school type is done by the parents and

is influenced by their SES, targeting integrated secondary schools may be important and could be em-

phasized more in health promotion activities. It appears that this may help to address the issue of indi-

vidual SES on the one hand (more children with low SES in secondary schools) but also neighbour-

hood SES on the other hand. Further research is needed to disentangle these complex relationships

between individual and neighbourhood SES, as well as school environment. With additional research

it could be investigated if some neighbourhoods might benefit more from screen time related activi-

ties, while others might benefit more from PA related activities. The aim should be the ability to target

the content of health promotion activities according to school type and neighbourhood to meet greatest

needs.

In addition to individual and neighbourhood SES, other factors like the built environment (i.e. number

of public transport stops, residential density, intersection density and the number of parks) could also

play an important role in adolescents’ health behaviours(73). These factors may be mediators of the

observed associations but studies have also suggested that associations may be moderated by the built

environment (studies have shown that individuals with low neighbourhood SES had a greater benefit

of good walkability than those with a high neighbourhood SES)(31,32). Future research should there-

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fore also include measurements of the built environment in Berlin to provide new insights into the

associations with PA.





teristics as Berlin: an urban well-connected region with relatively safe neighbourhoods and good infra-

structure for transportation and cycling.


up. However, we assessed one item of the FAS (holiday) additionally at baseline and at the 12 month

follow-up. The answers were quite similar over the two years. We thus think that the period of two

years was not associated with major changes in the FAS level. We also found differences in the self-

report FAS of boys and girls, which is somewhat surprising. It is possible that the structure of the

questionnaire led to an overestimation among boys due to a higher interest in cars and computers (i.e.

two key elements of the FAS). Second, PA was not measured objectively. Self-report of children and

adolescents, especially regarding PA, may lead to biased results through misreporting(75). Measure-

ment errors associated with self-report may further be influenced by SES of adolescents(76). Future

studies should use accelerometers or other means to objectively measure PA and sedentary

behaviour(77). Another limitation of our study is that we did not assess total sedentary behaviour and

that ST was determined based on the use of TV, Computer and video games as assessed by the HBSC

questionnaire(35,78). Other increasingly popular screen devices (e.g. smartphones, tablets) and other

kinds of sedentary behaviours like sitting during homework, talking on the phone and sitting at school

were not taken into account, which may have led to an underestimation of ST.

CONCLUSION

Lower individual and neighbourhood SES was associated with higher ST. Lower neighbourhood but

not individual SES was associated with higher PA. After consideration of school type and school

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neighbourhood SES associations were attenuated and became insignificant for the relationship be-

tween neighbourhood SES, PA and ST. Further research is warranted to unravel the complex relation-

ships between individual SES, neighbourhood SES and school environment to develop more targeted

health promotion strategies in the future.

ACKNOWLEDGEMENTS



CONTRIBUTORS






FUNDING


COMPETING INTERESTS

None declared.

ETHICS APPROVEL

The study was approved by the ethical review committee of the Charité-Universitätsmedizin Berlin, Germany.



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Figures



students (complete case analysis, n=1523)


(complete case analysis, n=1523)

Supplementary files


years old students (complete case analysis, n=1523)


old students (complete case analysis, n=1523)


years old students (unequal sample sizes)


old students (unequal sample sizes)

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254x338mm (300 x 300 DPI)

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Multivariable analysis of physical activity associated factors among 12 and 13 years old students (complete case analysis, n=1523)

380x331mm (300 x 300 DPI)

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Multivariable analysis of screen time associated factors among 12 and 13 years old stu-dents (complete case analysis, n=1523)

333x312mm (300 x 300 DPI)

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Model 1* Model 2**

Model 3***

Model 3a Model 3b



High 1 - - 1 1

Middle 0.98 (0.69; 1.38) - - 0.90 (0.63;1.29) 0.83 (0.58; 1.20)

Low 0.96 (0.54; 1.70) - - 0.85 (0.48; 1.52) 0.74 (0.41; 1.33)


High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.31 (0.85;2.04) 1.34 (0.86; 2.08) 1.19 (0.78; 1.82)

Low (rank 5-7) - - 1.70 (1.10;2.63) 1.76 (1.12; 2.75) 1.51 (0.93; 2.46)

Sex <0.001 <0.001 <0.001 <0.001

Boys 1 1 1 1

Girls 0.48 (0.34; 0.67) 0.47 (0.33;0.65) 0.47 (0.34; 0.66) 0.48 (0.34; 0.68)

BMI 0.92 (0.87; 0.98) 0.007 0.92 (0.86;0.98) 0.005 0.92 (0.86; 0.98) 0.006 0.91 (0.86; 0.97) 0.003


yes 1 1 1 1

no 0.87 (0.61; 1.25) 0.95 (0.66;1.37) 0.93 (0.65; 1.35) 0.94 (0.65; 1.37)

Screen time 0.429 0.311 - 0.334 0.233

≥2 hours 1 1 1 1

<2 hours 1.16 (0.81; 1.67) 1.21 (0.84;1.75) 1.20 (0.83; 1.74) 1.26 (0.86; 1.86)

School type - - - - - 0.002



School neighbourhood SES - - - - - 0.723

High (rank 1-2) - - - - - 1

Middle (rank 3-4) - - - - - 1.15 (0.69; 1.92)

Low (rank 5-7) - - - - - 0.92 (0.52; 1.62)

Complete case analysis (n=1523) *Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, screen time


***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+school type+school neighbourhood SES

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Model 1* Model 2**

Model 3***

Model 3a Model 3b



High 1 - - 1 1

Middle 1.36 (1.04; 1.78) - - 1.31 (1.00; 1.72) 1.25 (0.95; 1.64)

Low 2.25 (1.37; 3.68) - - 2.08 (1.26; 3.43) 1.88 (1.12; 3.14)


High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.08 (0.79;1.48) 1.03 (0.75; 1.41) 1.37 (0.99; 1.91)

Low (rank 5-7) - - 1.68 (1.20;2.35) 1.54 (1.10; 2.17) 1.40 (0.98; 2.00)

Sex <0.001 <0.001 <0.001

Boys 1 1 <0.001 1 1

Girls 0.43 (0.33; 0.55) 0.43 (0.33;0.56) 0.42 (0.32; 0.54) 0.42 (0.33; 0.54)

BMI 1.10 (1.05; 1.15) <0.001 1.11 (1.05;1.15) <0.001 1.10 (1.05; 1.15) <0.001 1.09 (1.04; 1.15) <0.001


yes 1 1 1 1

no 0.79 (0.59; 1.05) 0.79 (0.59;1.06) 0.84 (0.63; 1.12) 0.85 (0.63; 1.13)

Physical activity (PA) (WHO criteria fulfilled) 0.523 0.389 0.430 0.297

Yes 1 1 1 1

No 1.13 (0.78; 1.65) 1.18 (0.81;1.73) 1.17 (0.80; 1.70) 1.23 (0.84; 1.80)




School neighbourhood SES - - - - 0.535

High (rank 1-2) - - - - 1

Middle (rank 3-4) - - - - 1.26 (0.82; 1.92)

Low (rank 5-7) - - - - 1.07 (0.67; 1.70)

Complete case analysis (n=1523) *Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, PA


***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+ school type + school neighbourhood SES)

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High 1 - - 1 1

Middle 0.95 (0.69; 1.31) - - 0.89 (0.62; 1.26) 0.83 (0.58; 1.20)

Low 1.01 (0.60; 1.69) - - 0.87 (0.50; 1.54) 0.74 (0.41; 1.33)


High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.24 (0.84;1.84) 1.27 (0.85; 1.88) 1.19 (0.78; 1.82)

Low (rank 5-7) - - 1.67 (1.14;2.44) 1.65 (1.07; 2.56) 1.51 (0.93; 2.46)

Sex <0.001 <0.001 <0.001 <0.001

Boys 1 1 1 1

Girls 0.51 (0.38; 0.69) 0.48 (0.36;0.65) 0.48 (0.35; 0.67) 0.48 (0.34; 0.68)

BMI 0.90 (0.85; 0.95) <0.001 0.92 (0.87;0.97) 0.002 0.92 (0.87; 0.98) 0.006 0.91 (0.86; 0.97) 0.003


yes 1 1 1 1

no 0.97 (0.69; 1.36) 0.99 (0.72;1.36) 0.91 (0.63; 1.30) 0.94 (0.65; 1.37)

Screen time 0.734 0.165 - 0.425 0.233

≥2 hours 1 1 1 1

<2 hours 1.06 (0.76; 1.49) 1.26 (0.91;1.74) 1.16 (0.81; 1.67) 1.26 (0.86; 1.86)

School type - - - - - 0.002



School neighbourhood SES - - - - - 0.723

High (rank 1-2) - - - - - 1

Middle (rank 3-4) - - - - - 1.15 (0.69; 1.92)

Low (rank 5-7) - - - - - 0.92 (0.52; 1.62)

*Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, screen time


***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+ school type + school neighbourhood SES

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High 1 - - 1 1

Middle 1.28 (1.00; 1.64) - - 1.32 (1.01; 1.73) 1.25 (0.95; 1.64)

Low 2.03 (1.30; 3.15) - - 2.09 (1.27; 3.45) 1.88 (1.12; 3.14)


High (rank 1-2) - - 1 1 1

Middle (rank 3-4) - - 1.07 (0.80;1.44) 1.49 (1.08; 2.05) 1.37 (0.99; 1.91)

Low (rank 5-7) - - 1.68 (1.22;2.29) 1.55 (1.10; 2.17) 1.40 (0.98; 2.00)

Sex <0.001 <0.001 <0.001

Boys 1 1 <0.001 1 1

Girls 0.46 (0.37; 0.59) 0.45 (0.35;0.56) 0.42 (0.32; 0.54) 0.42 (0.33; 0.54)

BMI 1.09 (1.05; 1.14) <0.001 1.08 (1.04;1.13) <0.001 1.09 (1.04; 1.14) <0.001 1.09 (1.04; 1.15) <0.001


yes 1 1 1 1

no 0.75 (0.57; 0.98) 0.85 (0.65;1.11) 0.85 (0.63; 1.14) 0.85 (0.63; 1.13)


fulfilled) 0.738 0.189 0.510 0.297

Yes 1 1 1 1

No 1.06 (0.75; 1.50) 1.25 (0.90;1.74) 1.14 (0.78; 1.66) 1.23 (0.84; 1.80)




School neighbourhood SES - - - - 0.535

High (rank 1-2) - - - - 1

Middle (rank 3-4) - - - - 1.26 (0.82; 1.92)

Low (rank 5-7) - - - - 1.07 (0.67; 1.70)

*Model 1: individual socioeconomic status (FAS) adjusted for Sex, BMI, migration background, PA


***Model 3 (Model 3a: Model 1+2, Model 3b: Model 1+2+ school type + school neighbourhood SES)

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abstract



Introduction


reported


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cases and controls









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Data Sources/

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confounding





addressed


sampling strategy


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time




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Discussion







Other Information







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